11 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing

This chapter has papers on three-dimensional description methods. There is much overlap possible with the stereo chapter. There is a lot of work in the graphics field which would be relevant here, but is ignored. This chapter includes mostly computer vision applications of graphics representations. The primary theme is the generation of these representations, not the theory behind the ultimate representation. Descriptions, General.

11.1 Three-Dimensional Descriptions -- General

Chapter Contents (Back)
Descriptions, Three-Dimensional.
See also General Spatial Reasoning and Geometric Reasoning Issues, Visual Relations.
See also NURBS: Non-Uniform Rational B-Spline.
See also Implicit 3-D Models, Implicit Descriptions.

CVonline: Object, World and Scene Representations,
CV-OnlineJuly 2001.
HTML Version. Survey, Object Representation. BibRef 0107

NaturePix: Visual Cognitive Modeling Research,
2007.
WWW Link. Dataset, 3-D Data. ASU 3-D datasets. Replaces former ASU dataset?

EyeTronics,
2007
WWW Link. Vendor, Stereo Sensor. System for 3-D models from images. Also sells 3-D models.

3DSOM,
2007. 3D Software Object Modeller.
WWW Link. Vendor, Model Generation. System for 3-D models from images.

The Stanford 3D Scanning Repository,
2007.
WWW Link. Dataset, 3-D Data. Stanford graphics databases

AQSENSE,
1989.
WWW Link. Vendor, 3-D Shape. Code, 3-D Shape. Code for 3-D shape descriptions and inspection. Match 3-D, XYZ from range maps, merge range maps. The SAL3D (3D Shape Analysis Library) product.

The Beazley Archive of Classical Art Pottery Database,
July 2013
WWW Link. Dataset, Pottery.

Srihari, S.N.[Sargur N.],
Representation of Three-Dimensional Digital Images,
Surveys(13), No. 4, December 1981, pp. 399-424. Survey, Tomography. Tomography. Data structures. Among other things, compares octree type representation with others. BibRef 8112

Barnhill, R.E.,
A Survey of the Representation and Design of Surfaces,
IEEE_CGA(3), No. 7, October 1983, pp. 9-16. Survey, Representation. BibRef 8310

Aggarwal, J.K., Davis, L.S., Martin, W.N., Roach, J.W.,
Survey: Representation Methods in Three-Dimensional Objects,
PPR82(377-391). Survey, Representation. BibRef 8200

Besl, P.J.[Paul J.], and Jain, R.C.[Ramesh C.],
Three-Dimensional Object Recognition,
Surveys(17), No. 1, March 1985, pp. 75-145. Survey, Representation. BibRef 8503
Earlier:
Range Image Understanding,
CVPR85(430-449). Descriptions, Three-Dimensional. Recognize Three-Dimensional Objects. A survey of various techniques. BibRef

Besl, P.J.,
Geometric Modeling and Computer Vision,
PIEEE(76), No. 8, August 1988, pp. 936-958. BibRef 8808

Besl, P.J.,
Surfaces in Range Image Understanding,
Berlin: Springer-Verlag1988. BibRef 8800 Book BibRef
And: Add A2: Jain, R.C.,
Surface Characterization of Three-Dimensional Object Recognition in Depth Maps,
MichiganRSD-TR-20-84, 1984. More details covered in a variety of papers? BibRef

Besl, P.J.[Paul J.],
Geometric Signal Processing,
AIRI90(141-205). BibRef 9000

Forrest, A.R.[A. Robin],
On Coons' and Other Methods for the Representation of Curved Surfaces,
CGIP(1), No. 4, December 1972, pp. 341-359.
Elsevier DOI At the time, not a lot of Coons surface work had been published. A review. BibRef 7212

Jain, R., Aggarwal, J.K.,
Computer Analysis of Scenes with Curved Objects,
PIEEE(67), 1979, pp. 805-812. BibRef 7900

Chiyokura, B., Kimura, F.,
Design of Solids with Free-Form Surfaces,
Computer Graphics(17), No. 3, 1983, pp. 289-293. BibRef 8300

Reed, G.M.[George M.],
On the Characterization of Simple Closed Surfaces in Three-Dimensional Digital Images,
CVGIP(25), No. 2, February 1984, pp. 226-235.
Elsevier DOI BibRef 8402

Reed, G.M.[George M.], Rosenfeld, A.,
Recognition of Surfaces in three-Dimensional Digital Imgaes,
InfoControl(53), 1982, pp. 108-120. BibRef 8200
Earlier: UMD-TR-1210, August 1982. BibRef

Morgenthaler, D.G., and Rosenfeld, A.,
Surfaces in Three-Dimensional Digital Images,
InfoControl(51), 1981, pp. 227-247. BibRef 8100
Earlier: UMD-TR-940, September 1980. BibRef

Bowyer, K.W., Dyer, C.R.,
Three-Dimensional Shape Representation,
HPRIP-CV94(17-51). BibRef 9400

Nishihara, H.K.,
Intensity, Visible-Surface, and Volumetric Representation,
AI(17), No. 1-3, August 1981, pp. 265-284.
Elsevier DOI A variety of 3-D representation techniques are discussed with examples from MIT work. BibRef 8108

Yamamoto, K.,
Future Directions in Computer Vision and Image Understanding: ETL Perspectives,
ICPR90(I: 32-37).
IEEE DOI Mostly 3-D processing work. BibRef 9000

Ullmann, J.R.,
An Investigation of Occlusion in One Dimension,
CVGIP(22), No. 1, April 1983, pp. 194-203.
Elsevier DOI Two-D occlusions:
See also Analysis of 2-D Occlusion by Subtracting Out. BibRef 8304

Voss, K.,
Images, Objects, and Surfaces in Z[N],
PRAI(5), 1991, pp. 797-808. BibRef 9100

Flinchbaugh, B.E.[Bruce E.],
System and method for displaying, and interactively excavating and examining a three dimensional volume,
US_Patent4,685,070, August 17, 1993.
HTML Version. Display 2-D representation of 3-D object. Tesselation of cells. BibRef 9308

Fisher, R.B.,
Representing 3D Structures for Visual Recognition,
AIR(1), No. 3, 1987, pp. 183-200. BibRef 8700 Edinburgh Survey, Representation. Survey of visual model representations
See also SMS: A Suggestive Modelling System for Object Recognition. BibRef

Maybank, S.J.,
The projective geometry of ambiguous surfaces,
Royal(A: 332), 1990, pp. pp. 1-47. BibRef 9000

Faugeras, O.D.,
Stratification of 3-Dimensional Vision: Projective, Affine, and Metric Representations,
JOSA-A(12), No. 3, March 1995, pp. 465-484.
See also Few Steps Toward Artificial 3-D Vision, A. BibRef 9503

Bajcsy, R.,
Signal-to-Symbol Transformation and Vice-Versa: From Fundamental Processes to Representation,
Surveys(27), No. 3, September 1995, pp. 310-313. Survey, Representation. BibRef 9509

Chen, P.C., Tsai, W.C., Hwang, S.Y.,
A Graded Approach to Shape Representation,
JVCIR(7), No. 2, June 1996, pp. 105-115. 9607
BibRef

Watt, R.J.,
Issues in Shape Perception,
IVC(11), No. 6, July-August 1993, pp. 389-394.
Elsevier DOI BibRef 9307

Mohr, R.,
Projective Geometry and Computer Vision,
HPRCV97(Chapter II:4). (LIFIA, France) BibRef 9700

Loncaric, S.[Sven],
A Survey of Shape Analysis Techniques,
PR(31), No. 8, August 1998, pp. 983-1001.
Elsevier DOI 9807
Survey, Shape. BibRef

Lengyel, J.[Jed],
The Convergence of Graphics and Vision,
Computer(31), No. 7, July 1998, pp. 46-53. Survey, Graphics and Vision. Survey of the similarities. Vision generates the models, graphics assume the models. Both have a spectrum of image-based to physical-based techniques, attack from opposite ends. BibRef 9807

Goshtasby, A.A.[A. Ardeshir], Sonka, M.[Milan], Udupa, J.K.[Jayaram K.],
Analysis of Volumetric Images,
CVIU(77), No. 2, February 2000, pp. 79-83.
DOI Link 0003
BibRef

Paquet, E.[Eric], Rioux, M.[Marc], Murching, A.[Anil], Naveen, T.[Thumpudi], Tabatabai, A.[Ali],
Description of shape information for 2-D and 3-D objects,
SP:IC(16), No. 1-2, September 2000, pp. 103-122.
Elsevier DOI 0008
BibRef

Campbell, R.J.[Richard J.], Flynn, P.J.[Patrick J.],
A Survey of Free-Form Object Representation and Recognition Techniques,
CVIU(81), No. 2, February 2001, pp. 166-210.
DOI Link 0103
Survey, Object Representation. BibRef
Earlier:
Eigenshapes for 3D Object Recognition in Range Data,
CVPR99(II: 505-510).
IEEE DOI Extend appearance based to range data. BibRef

Oliva, A.[Aude], Torralba, A.B.[Antonio B.],
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope,
IJCV(42), No. 3, May-June 2001, pp. 145-175.
DOI Link
WWW Link. 0108
Dataset, Outdoor Secens. BibRef
Earlier:
Scene-Centered Description from Spatial Envelope Properties,
BMCV02(263 ff.).
Springer DOI 0303
Otherwise known as OSR dataset. Spatial envelope: low dimensional representation of the secen. Perceptual dimensions to represent the dominat satial structure. BibRef

Pastor, L.[Luis], Rodríguez, A.[Angel], Espadero, J.M.[J. Miguel], Rincón, L.[Luis],
3D wavelet-based multiresolution object representation,
PR(34), No. 12, December 2001, pp. 2497-2513.
Elsevier DOI 0110
Award, Pattern Recognition, Honorable Mention. BibRef

Pastor, L.[Luis], Rodríguez, A.[Angel],
Surface approximation of 3D objects from irregularly sampled clouds of 3D points using spherical wavelets,
CIAP99(70-75).
IEEE DOI 9909
BibRef

Zribi, M.[Mourad],
Description of three-dimensional gray-level objects by the harmonic analysis approach,
PRL(23), No. 1-3, January 2002, pp. 235-243.
Elsevier DOI 0201
BibRef

Brimkov, V.E.[Valentin E.], Andres, E.[Eric], Barneva, R.P.[Reneta P.],
Object Discretizations in Higher Dimensions,
PRL(23), No. 6, April 2002, pp. 623-636.
Elsevier DOI 0202

See also Advances in combinatorial image analysis. BibRef

Brimkov, V.E.[Valentin E.],
Connectedness of Offset Digitizations in Higher Dimensions,
CompIMAGE10(36-46).
Springer DOI 1006
BibRef

Brimkov, V.E.[Valentin E.], Barneva, R.P.[Reneta P.],
Polyhedrization of Discrete Convex Volumes,
ISVC06(I: 548-557).
Springer DOI 0611
BibRef

Brimkov, V.E.[Valentin E.], Maimone, A.[Angelo], Nordo, G.[Giorgio],
On the Notion of Dimension in Digital Spaces,
IWCIA06(241-252).
Springer DOI 0606
BibRef

Brimkov, V.E.[Valentin E.],
Digitization scheme that assures faithful reconstruction of plane figures,
PR(42), No. 8, August 2009, pp. 1637-1649.
Elsevier DOI 0904
BibRef
Earlier:
Scaling of Plane Figures That Assures Faithful Digitization,
IWCIA08(xx-yy).
Springer DOI 0804
Digital geometry; Lattice polygon; Scaling factor; Polyhedral reconstruction; NP-hard problem; Object reconstruction BibRef

Andres, E.[Eric],
Discrete linear objects in dimension n: the standard model,
GM(65), No. 1-3, May 2003, pp. 92-111.
Elsevier DOI 0309
BibRef

Sato, Y.[Yoichi], Ikeuchi, K.[Katsushi], (Eds.)
Modeling from Reality (Book),
KluwerBoston, November 2001. ISBN 0-7923-7515-7. Ref as: BibRef 0111 MfR01
WWW Link. Mostly chapters from past papers, research at Tokyo and CMU. Turn real objects and environments into 3-D models for VR systems. BibRef

Ikeuchi, K.,
Modeling from reality,
3DIM01(117-124).
IEEE DOI 0106

See also Towards an Assembly Plan from Observation, Part I: Task Recognition with Polyhedral Objects. BibRef

Leyton, M.[Michael],
A Generative Theory of Shape,
Springer-VerlagOctober 2001. ISBN 3-540-42717-1.
Springer DOI Maximize transfer of structure and recoverability of generative operations. BibRef 0110

Leyton, M.[Michael],
The Structure of Paintings,
Springer2006, ISBN 978-3-211-35739-2
Springer DOI Shape is equivalent to memory storage. A principal argument of these foundations is that artworks are maximal memory stores. BibRef 0600

Defez, E., Villanueva-Oller, J., Villanueva, R.J., Law, A.,
Matrix Cubic Splines for Progressive 3D Imaging,
JMIV(17), No. 1, July 2002, pp. 41-53.
DOI Link 0211
BibRef

Marques, F.[Ferran], Lavagetto, F.[Fabian], Strintzis, M.G.[Michael G.],
Image processing for 3D imaging,
SP:IC(17), No. 9, October 2002, pp. 653-655.
Elsevier DOI 0211
BibRef

Galpin, F.[Franck], Morin, L.[Luce],
Sliding Adjustment for 3D Video Representation,
JASP(2002), No. 10, October 2002, pp. 1088-1101.
WWW Link. 0211
BibRef
Earlier:
Video Coding Using Streamed 3d Representation,
ICIP00(Vol III: 636-639).
IEEE DOI 0008
BibRef

Balter, R., Gioia, P., Morin, L., Galpin, F.,
Scalable and efficient coding of 3d model extracted from a video,
3DPVT04(836-843).
IEEE DOI 0412
BibRef

Galpin, F., Balter, R., Morin, L., Deguchi, K.,
3D models coding and morphing for efficient video compression,
CVPR04(I: 334-341).
IEEE DOI 0408
BibRef

de Kemp, E.A.[Eric A.], Sprague, K.B.[Kevin B.],
Interpretive Tools for 3-D Structural Geological Modeling Part I: Bézier-Based Curves, Ribbons and Grip Frames,
GeoInfo(7), No. 1, March 2003, pp. 55-71.
DOI Link 0304
BibRef

Sprague, K.B.[Kevin B.], de Kemp, E.A.[Eric A.],
Interpretive Tools for 3-D Structural Geological Modelling Part II: Surface Design from Sparse Spatial Data,
GeoInfo(9), No. 1, March 2005, pp. 5-32.
Springer DOI 0509
BibRef

Abrams, L., Fishkind, D.E., Priebe, C.E.,
A proof of the spherical homeomorphism conjecture for surfaces,
MedImg(21), No. 12, December 2002, pp. 1564-1566.
IEEE Top Reference. 0301
BibRef

Abrams, L., Fishkind, D.E., Priebe, C.E.,
The generalized spherical homeomorphism theorem for digital images,
MedImg(23), No. 5, May 2004, pp. 655-657.
IEEE Abstract. 0406
Spherical homeomorphism conjecture (
See also Automated graph-based analysis and correction of cortical volume topology. ). MRI reconstruction. BibRef

Sun, Y.Y.[Yi-Yong], Paik, J.K.[Joon-Ki], Koschan, A.F., Page, D.L., Abidi, M.A.,
Point fingerprint: A new 3-D object representation scheme,
SMC-B(33), No. 4, August 2003, pp. 712-717.
IEEE Abstract. 0308
BibRef

Csakany, P., Wallace, A.M.,
Representation and classification of 3-D objects,
SMC-B(33), No. 4, August 2003, pp. 638-647.
IEEE Abstract. 0308
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Alterson, R.[Robert], Spetsakis, M.E.[Minas E.],
Object recognition with adaptive Gabor features,
IVC(22), No. 12, 1 October 2004, pp. 1007-1014.
Elsevier DOI 0409
BibRef
Earlier:
An Adaptive-Sampling Algorithm for Object Representation,
VI02(1).
PDF File. 0208
BibRef

Rushmeier, H.E., and Bernardini, F.,
The 3D model acquisition pipeline,
CGForum(21), No. 2, 2002, pp. 149-172. Describes the techniques tobuild 3D model of real objects. BibRef 0200

Bernardini, F., Mittleman, J., Rushmeier, H.E., Silva, C., Taubin, G.,
The Ball-Pivoting Algoritm for Surface Reconstruction,
VCG(5), 1999, pp. 349-359.
IEEE DOI 1408
For Code:
See also Analysis and Implementation of a Parallel Ball Pivoting Algorithm, An. Also see:
See also Implementation and Parallelization of the Scale Space Meshing Algorithm, An. BibRef

Rushmeier, H.E.,
3D capture for computer graphics,
3DIM01(375-381).
IEEE DOI 0106
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Zanuttigh, P.[Pietro], Brusco, N.[Nicola], Taubman, D.S.[David S.], Cortelazzo, G.M.[Guido M.],
A novel framework for the interactive transmission of 3D scenes,
SP:IC(21), No. 9, October 2006, pp. 787-811.
Elsevier DOI 0611
BibRef
Earlier:
Greedy Non-Linear Approximation of the Plenoptic Function for Interactive Transmission of 3D Scenes,
ICIP05(I: 629-632).
IEEE DOI 0512
3D Scene compression; Interactive browsing; Scene rendering; Wavelet transforms; JPEG 2000; Distortion modelling BibRef

Zanuttigh, P.[Pietro], Brusco, N.[Nicola], Cortelazzo, G.M.[Guido M.], Taubman, D.S.[David S.],
A Rate Distortion Framework for 3D Browsing,
3DTV07(1-4).
IEEE DOI 0705
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Darom, T., Ruggeri, M.R., Saupe, D., Kiryati, N.,
Compression of textured surfaces represented as surfel sets,
SP:IC(21), No. 9, October 2006, pp. 770-786.
Elsevier DOI 0611
BibRef
Earlier:
Processing of Textured Surfaces Represented as Surfel Sets: Representation, Compression and Geodesic Paths,
ICIP05(I: 605-608).
IEEE DOI 0512
Textured surface compression; Spherical mapping; Geodesic paths; Surfels; Spherical wavelets BibRef

Ruggeri, M.R.[Mauro R.], Patanè, G.[Giuseppe], Spagnuolo, M.[Michela], Saupe, D.[Dietmar],
Spectral-Driven Isometry-Invariant Matching of 3D Shapes,
IJCV(89), No. 2-3, September 2010, pp. xx-yy.
Springer DOI 1006

See also Spectral feature selection for shape characterization and classification. BibRef

Patané, G.[Giuseppe],
Laplacian spectral distances and kernels on 3D shapes,
PRL(47), No. 1, 2014, pp. 102-110.
Elsevier DOI 1408
Spectral distances BibRef

Patanè, G.[Giuseppe],
Fourier-Based and Rational Graph Filters for Spectral Processing,
PAMI(45), No. 6, June 2023, pp. 7063-7074.
IEEE DOI 2305
Convolution, Laplace equations, Kernel, Fourier transforms, Chebyshev approximation, Eigenvalues and eigenfunctions, spectral graph processing BibRef

Patané, G.[Giuseppe],
A unified definition and computation of Laplacian spectral distances,
PR(93), 2019, pp. 68-78.
Elsevier DOI 1906
Laplacian spectrum, Spectral distances, Spectral kernels, Heat kernel, Diffusion distances and geometry, Shape and graph analysis BibRef

Ruggeri, M.R.[Mauro R.], Saupe, D.[Dietmar],
Isometry-Invariant Matching Of Point Set Surfaces,
3DOR08(17-24)
DOI Link 1301
BibRef

Klette, R., Kozera, R., Noakes, L., Weickert, J., (Eds.)
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Springer2006, ISBN 978-1-4020-3857-0
WWW Link. The area of high-accuracy measurements of length, curvature, motion parameters and other geometrical quantities from acquired image data. BibRef 0600

Krim, H.[Hamid], Yezzi, Jr., A.J.[Anthony J.], (Eds.)
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WWW Link. Medial axis, skeletons, point clouds, curves, perspective, high dimensional spaces, shape metrics. BibRef 0600

de Floriani, L.[Leila], Spagnuolo, M.[Michela], (Eds.)
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Ozaktas, H.M.[Haldun M.], Onural, L.[Levent], (Eds.)
Three-Dimensional Television: Capture, Transmission, Display,
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Samet, H.[Hanan],
Object-based and image-based object representations,
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WWW Link. 0805
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Peters, J.[Jörg], Reif, U.[Ulrich],
Subdivision Surfaces,
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Eisert, P.[Peter], Pollefeys, M.[Marc], Tubaro, S.[Stefano],
Three-Dimensional Image and Video Processing, Intro,
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Toriwaki, J.[Junichiro], Yoshida, H.[Hiroyuki],
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Springer2009, ISBN: 978-1-84800-172-5
WWW Link. Survey, 3-D. Survey, Representation. 0905
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Kordelas, G.[Georgios], Daras, P.[Petros],
Viewpoint independent object recognition in cluttered scenes exploiting ray-triangle intersection and SIFT algorithms,
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Earlier:
Robust SIFT-based feature matching using Kendall's rank correlation measure,
ICIP09(325-328).
IEEE DOI 0911
BibRef
Earlier:
Recognizing 3D Objects using Ray-Triangle Intersection Distances,
ICIP07(VI: 173-176).
IEEE DOI 0709
Based on distance from circular region and points on the object. 3D object recognition; Distance maps; Ray-triangle intersection; Clutter; Occlusion BibRef

Zarpalas, D.[Dimitris], Kordelas, G.[Georgios], Daras, P.[Petros],
Recognizing 3D objects in cluttered scenes using projection images,
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Tyler, C.W.[Christopher W.], (Ed.)
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Tyler, C.W.[Christopher W.], Nicholas, S.C.[Spero C.],
Perceptual coding for 3D reconstruction,
EUVIP11(116-121).
IEEE DOI 1110
Encoding for the 3-D computations. BibRef

Miao, Y.W.[Yong-Wei], Feng, J.Q.[Jie-Qing],
Perceptual-saliency extremum lines for 3D shape illustration,
VC(26), No. 6-8, June 2010, pp. 433-443.
WWW Link. 1101
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Rustamov, R.M.[Raif M.],
A versatile framework for shape description,
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WWW Link. 1101
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Earlier:
Robust Volumetric Shape Descriptor,
EG3DOR10(1-5)
DOI Link 1301
BibRef
Earlier:
Template Based Shape Descriptor,
3DOR09(1-7)
DOI Link 1301
3D shape retrieval - Numerical shape descriptors - Barycentric coordinates - Laplace-Beltrami eigenfunctions - Diffusion wavelets From surface mesh. BibRef

Rustamov, R.M.[Raif M.],
Interpolated eigenfunctions for volumetric shape processing,
VC(27), No. 11, November 2011, pp. 951-961.
WWW Link. 1112
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Yu, F., Lu, Z., Luo, H., Wang, P.,
Three-Dimensional Model Analysis and Processing,
Springer2010, ISBN: 978-3-642-12650-5
WWW Link. 1101
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libE57: software tools for managing E57 files,
OnlineDecember 11, 2010.
WWW Link. Code, 3D Data. BibRef 1012

Huber, D.F.[Daniel F.],
A new, open standard for 3D imaging data,
SPIE(Newsroom), January 12, 2011.
DOI Link 1101
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Edwards, J.,
Three-Dimensional Research Adds New Dimensions,
SPMag(28), No. 3, 2011, pp. 10-13.
IEEE DOI 1105
Survey, 3-D Research. Special Reports BibRef

Smith, E.R.[Eric R.], Radke, R.J.[Richard J.], Stewart, C.V.[Charles V.],
Physical Scale Keypoints: Matching and Registration for Combined Intensity/Range Images,
IJCV(97), No. 1, March 2012, pp. 2-17.
WWW Link. 1202
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Earlier:
Physical Scale Intensity-Based Range Keypoints,
3DPVT10(xx-yy).
WWW Link. 1005
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Yapo, T.C.[Theodore C.], Stewart, C.V.[Charles V.], Radke, R.J.[Richard J.],
A probabilistic representation of LiDAR range data for efficient 3D object detection,
S3D08(1-8).
IEEE DOI 0806
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Abate, M., Tovena, F.,
Curves and Surfaces,
Springer2012, ISBN: 978-88-470-1940-9


WWW Link. 1205
Differential Geometry of Curves and Surfaces BibRef

Pears, N.E.[Nick E.], Liu, Y.H.[Yong-Huai], Bunting, P.[Peter], (Eds.)
3D Imaging, Analysis and Applications,
Springer2012, ISBN: 978-1-4471-4062-7


WWW Link. 1205
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Rusu, R.B.[Radu Bogdan],
Semantic 3D Object Maps for Everyday Robot Manipulation,
Springer2013. ISBN 978-3-642-35478-6


WWW Link. 1304
3D Models for a robot's operating environment. BibRef

Breuß, M.[Michael], Bruckstein, A.M.[Alfred M.], Maragos, P.[Petros], (Eds.)
Innovations for Shape Analysis: Models and Algorithms,
Springer2013. ISBN 978-3-642-34140-3.


WWW Link. 1304
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Förstner, W.[Wolfgang],
Graphical Models in Geodesy and Photogrammetry,
PFG(2013), No. 4, 2013, pp. 255-267.
DOI Link 1309
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Scene representation BibRef

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Xie, J.[Jin], Zhu, F.[Fan], Dai, G.X.[Guo-Xian], Shao, L., Fang, Y.[Yi],
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Xie, J.[Jin], Dai, G.X.[Guo-Xian], Zhu, F.[Fan], Wong, E.K.[Edward K.], Fang, Y.[Yi],
DeepShape: Deep-Learned Shape Descriptor for 3D Shape Retrieval,
PAMI(39), No. 7, July 2017, pp. 1335-1345.
IEEE DOI 1706
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Deepshape: Deep learned shape descriptor for 3D shape matching and retrieval,
CVPR15(1275-1283)
IEEE DOI 1510
Feature extraction, Heating, Kernel, Neurons, Shape, Solid modeling, 3D shape retrieval, Fisher discrimination criterion, auto-encoder, heat diffusion, heat kernel signature. BibRef

Xie, J.[Jin], Dai, G.X.[Guo-Xian], Fang, Y.[Yi],
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IEEE DOI 1710
Feature extraction, Manifolds, Measurement, Neural networks, Shape, Solid modeling, 3D shape descriptor, 3D shape retrieval, deep neural network, metric learning, multiple, shape, features BibRef

Dai, G.X.[Guo-Xian], Xie, J., Fang, Y.,
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IEEE DOI 1805
Computational modeling, Feature extraction, Measurement, Shape, Solid modeling, mitigate BibRef

Xie, J.[Jin], Dai, G.X.[Guo-Xian], Zhu, F.[Fan], Shao, L., Fang, Y.[Yi],
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IEEE DOI 1801
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CVPR17(3615-3623)
IEEE DOI 1711
Encoding, Feature extraction, Heating, Kernel, Measurement, Shape, Solid modeling, 3-D shape descriptor, 3-D shape retrieval, neural network. Probability distribution, Visualization BibRef

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healthy versus pathological heart morphologies, and iii) lumbar vertebrae. Data models, Manifolds, Principal component analysis, Probability density function, Shape, Sociology, variational Bayes BibRef

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IEEE DOI 1705
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Shape, Estimation, Solid modeling, Computational modeling, 3D reconstruction BibRef

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Using labeled 2-D images with 3-D models for descriptions. Solid modeling, Adaptation models, Computational modeling, Data models, domain adaptation BibRef

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Solid modeling, Context modeling, Data models, Analytical models, Deep learning, Feature extraction, unsupervised feature learning BibRef

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Deep learning, Generative models, Normalizing flows, Point cloud modeling BibRef

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Shape, Feature extraction, Task analysis, Surface reconstruction, Training, Surface treatment, 3D deep learning BibRef

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3D Shape analysis, Isometry invariant, Non-rigid BibRef

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Data in a 3D rotation group SO_3. Quaternions, Skeleton, Data models, Point cloud compression, Solid modeling, Computational modeling, Quaternion product units, 3D rotation modeling BibRef

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Discrete geometry, BCC Grid, Discrete analytical plane, Discrete analytical sphere, Discrete analytical line, 3D Coordinate system BibRef

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Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis,
PAMI(45), No. 8, August 2023, pp. 9726-9742.
IEEE DOI 2307
Irregular 3D point clouds of arbitrary geometry and topology as a completely regular 2D point geometry image. Point cloud compression, Feature extraction, Geometry, Task analysis, Surface treatment, Solid modeling, unsupervised learning BibRef

Liu, J.X.[Jin-Xian], Ni, B.B.[Bing-Bing], Chen, Y.[Ye], Yu, Z.B.[Zhen-Bo], Wang, H.[Hang],
Learning by Restoring Broken 3D Geometry,
PAMI(45), No. 9, September 2023, pp. 11024-11039.
IEEE DOI 2309
self-supervised 3D learning. Learn via breaking the object. Apply to new domain. BibRef

Ogayar-Anguita, C.J.[Carlos J.], López-Ruiz, A.[Alfonso], Segura-Sánchez, R.J.[Rafael J.], Rueda-Ruiz, A.J.[Antonio J.],
A Version Control System for Point Clouds,
RS(15), No. 18, 2023, pp. 4635.
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Deep-Learning-Based 3-D Surface Reconstruction: A Survey,
PIEEE(111), No. 11, November 2023, pp. 1464-1501.
IEEE DOI 2311
Survey, Surface Reconstruction. BibRef

Clementini, E.[Eliseo], Cohn, A.G.[Anthony G.],
Extension of RCC*-9 to Complex and Three-Dimensional Features and Its Reasoning System,
IJGI(13), No. 1, 2024, pp. 25.
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RCC*-9 is a mereotopological qualitative spatial calculus for simple lines and regions. BibRef

Zhou, Y.[Yan], Sun, H.J.[Hua-Jie], Zhang, H.D.[Huai-Dong], Xu, X.M.[Xue-Miao], Yi, C.[Chang'an], Ye, D.[Dewang], Zhou, Y.X.[Yue-Xia], Liu, X.Y.[Xiang-Yu],
GaFL: Geometric-aware Feature Learning for universal 3D models recognition,
PR(149), 2024, pp. 110214.
Elsevier DOI 2403
Universal 3D models recognition, Geometric feature, Spherical convolution, Inactivation fusion BibRef


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FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction,
ICCV23(18377-18386)
IEEE DOI 2401
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Stathopoulos, A.[Anastasis], Pavlakos, G.[Georgios], Han, L.G.[Li-Gong], Metaxas, D.N.[Dimitris N.],
Learning Articulated Shape with Keypoint Pseudo-Labels from Web Images,
CVPR23(13092-13101)
IEEE DOI 2309
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Xue, L.[Le], Gao, M.F.[Ming-Fei], Xing, C.[Chen], Martín-Martín, R.[Roberto], Wu, J.J.[Jia-Jun], Xiong, C.M.[Cai-Ming], Xu, R.[Ran], Niebles, J.C.[Juan Carlos], Savarese, S.[Silvio],
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding,
CVPR23(1179-1189)
IEEE DOI 2309
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Li, X.[Xiang], Wen, C.C.[Cong-Cong], Huang, H.[Hao],
Unsupervised 3d Shape Representation Learning Using Normalizing Flow,
ACCV22(I:158-175).
Springer DOI 2307
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Zhang, T.H.[Tun-Hou], Ma, M.Y.[Ming-Yuan], Yan, F.[Feng], Li, H.[Hai], Chen, Y.[Yiran],
Joint Point Interaction-Dimension Search for 3D Point Cloud,
WACV23(1298-1307)
IEEE DOI 2302
Point cloud compression, Geometry, Solid modeling, Semantic segmentation, Computational modeling, Robotics BibRef

Karmakar, N.[Nilanjana], Biswas, A.[Arindam], Nandy, S.C.[Subhas C.], Bhattacharya, B.B.[Bhargab B.],
On the Construction of Planar Embedding for a Class of Orthogonal Polyhedra,
IWCIA22(84-104).
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2D-representations of 3D digital objects. BibRef

Low, W.F.[Weng Fei], Lee, G.H.[Gim Hee],
Minimal Neural Atlas: Parameterizing Complex Surfaces with Minimal Charts and Distortion,
ECCV22(II:465-481).
Springer DOI 2211
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Long, X.X.[Xiao-Xiao], Lin, C.[Cheng], Wang, P.[Peng], Komura, T.[Taku], Wang, W.P.[Wen-Ping],
SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views,
ECCV22(XXXII:210-227).
Springer DOI 2211
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Morreale, L.[Luca], Aigerman, N.[Noam], Guerrero, P.[Paul], Kim, V.G.[Vladimir G.], Mitra, N.J.[Niloy J.],
Neural Convolutional Surfaces,
CVPR22(19311-19320)
IEEE DOI 2210
Geometry, Image coding, Dictionaries, Smoothing methods, Shape, Pipelines, Representation learning, Vision + graphics BibRef

Murphy, K.A.[Kieran A.], Jampani, V.[Varun], Ramalingam, S.[Srikumar], Makadia, A.[Ameesh],
Learning ABCs: Approximate Bijective Correspondence for isolating factors of variation with weak supervision,
CVPR22(15989-15999)
IEEE DOI 2210
Deep learning, Codes, Annotations, Focusing, Partitioning algorithms, Pattern recognition, Representation learning, Self- semi- meta- unsupervised learning BibRef

Athar, A.[Ali], Luiten, J.[Jonathon], Hermans, A.[Alexander], Ramanan, D.[Deva], Leibe, B.[Bastian],
HODOR: High-level Object Descriptors for Object Re-Segmentation in Video Learned from Static Images,
CVPR22(3012-3021)
IEEE DOI 2210
Training, Image coding, Codes, Annotations, Image annotation, Object segmentation, Video analysis and understanding, Self- semi- meta- unsupervised learning BibRef

Qiu, Z.F.[Zhao-Fan], Yao, T.[Ting], Ngo, C.W.[Chong-Wah], Mei, T.[Tao],
MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing,
CVPR22(3052-3062)
IEEE DOI 2210
Visualization, Transformers, Pattern recognition, Complexity theory, Video analysis and understanding BibRef

Rabab, O.[Ouchker], Tahiri, M.A.[Mohamed Amine], Bencherqui, A.[Ahmed], Amakdouf, H.[Hicham], Jamil, M.O.[Mohamed Ouazzani], Qjidaa, H.[Hassan],
Efficient Localization And Reconstruction Of 3D Objects Using The New Hybrid Squire Moment,
ISCV22(1-8)
IEEE DOI 2208
Location awareness, Simulation, Intelligent systems, Image reconstruction, reconstruction BibRef

Shan, M.[Mo], Feng, Q.J.[Qiao-Jun], Jau, Y.Y.[You-Yi], Atanasov, N.[Nikolay],
ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description,
ICCV21(5926-5935)
IEEE DOI 2203
Geometry, Simultaneous localization and mapping, Shape, Computational modeling, Semantics, Vision for robotics and autonomous vehicles BibRef

Loiseau, R.[Romain], Monnier, T.[Tom], Aubry, M.[Mathieu], Landrieu, L.[Loïc],
Representing Shape Collections With Alignment-Aware Linear Models,
3DV21(1044-1053)
IEEE DOI 2201
Point cloud compression, Deep learning, Solid modeling, Codes, Shape, Neural networks BibRef

Otero, R., Lagüela, S., Arias, P.,
Algorithm for the Counterclockwise Ordering of Vertexes of Slanted Surfaces Towards the Generation of Semantic GBXML Models,
ISPRS21(B4-2021: 375-381).
DOI Link 2201
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Chen, G.J.[Gao-Jie], Sun, R.[Ran], Ma, J.[Jie], Wu, B.L.[Bing-Li],
Attention-Based Local Region Aggregation Network for Hierarchical Point Cloud Learning,
ICIP21(3093-3097)
IEEE DOI 2201
Shape, Image processing, Aggregates, Benchmark testing, Point cloud, Region aggregation, Attention mechanism, Hierarchical network BibRef

Yao, S.[Shun], Yang, F.[Fei], Cheng, Y.M.[Yong-Mei], Mozerov, M.G.[Mikhail G.],
3D Shapes Local Geometry Codes Learning with SDF,
DLGC21(2110-2117)
IEEE DOI 2112
Signed Distance Function for 3D descriptions. Geometry, Training, Measurement, Solid modeling, Codes, Shape BibRef

Ali, S.[Sharjeel], van Kaick, O.[Oliver],
Evaluation of Latent Space Learning with Procedurally-Generated Datasets of Shapes,
DLGC21(2086-2094)
IEEE DOI 2112
Solid modeling, Shape, Computational modeling, Shape measurement, Neural networks BibRef

Mihajlovic, M.[Marko], Weder, S.[Silvan], Pollefeys, M.[Marc], Oswald, M.R.[Martin R.],
DeepSurfels: Learning Online Appearance Fusion,
CVPR21(14519-14530)
IEEE DOI 2111
Geometry, Runtime, Shape, Scalability, Pipelines, Machine learning, Rendering (computer graphics) BibRef

Lal, S.[Shamit], Prabhudesai, M.[Mihir], Mediratta, I.[Ishita], Harley, A.W.[Adam W.], Fragkiadaki, K.[Katerina],
CoCoNets: Continuous Contrastive 3D Scene Representations,
CVPR21(12482-12491)
IEEE DOI 2111
Training, Visualization, Solid modeling, Object detection, Predictive models, Feature extraction BibRef

Yang, M.Y.[Ming-Yue], Wen, Y.X.[Yu-Xin], Chen, W.K.[Wei-Kai], Chen, Y.W.[Yong-Wei], Jia, K.[Kui],
Deep Optimized Priors for 3D Shape Modeling and Reconstruction,
CVPR21(3268-3277)
IEEE DOI 2111
Training, Solid modeling, Shape, Particle measurements, Time measurement BibRef

Paschalidou, D.[Despoina], Katharopoulos, A.[Angelos], Geiger, A.[Andreas], Fidler, S.[Sanja],
Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks,
CVPR21(3203-3214)
IEEE DOI 2111
Geometry, Solid modeling, Shape, Computational modeling, Neural networks BibRef

Chen, X.T.[Xiao-Tian], Wang, Y.W.[Yu-Wang], Chen, X.J.[Xue-Jin], Zeng, W.J.[Wen-Jun],
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation,
CVPR21(3033-3042)
IEEE DOI 2111
Training, Geometry, Semantics, Estimation, Feature extraction BibRef

Liu, Z.Y.[Ze-Yu], Liu, J.W.[Jian-Wei], Zuo, X.[Xin], Li, W.M.[Wei-Min],
Learning 3D-Craft Generation with Predictive Action Neural Network,
MMMod21(I:541-553).
Springer DOI 2106
Construct houses in Minecraft environment. BibRef

Tattersall, G.[George], Zhu, D.Z.[Di-Zhong], Smith, W.A.P.[William A. P.], Deterding, S.[Sebastian], Huber, P.[Patrik],
Reconstructing Creative Lego Models,
ACCV20(I:55-70).
Springer DOI 2103
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Zhang, J., Yu, M.Y., Vasudevan, R., Johnson-Roberson, M.,
Learning Rotation-Invariant Representations of Point Clouds Using Aligned Edge Convolutional Neural Networks,
3DV20(200-209)
IEEE DOI 2102
Task analysis, Training, Convolution, Shape, Sensors, Point Cloud, Deep Learning BibRef

Maboudi, M., Gerke, M., Hack, N., Brohmann, L., Schwerdtner, P., Placzek, G.,
Current Surveying Methods for the Integration of Additive Manufacturing In the Construction Process,
ISPRS20(B4:763-768).
DOI Link 2012
To create the basic conditions for the introduction of additive manufacturing in construction, and thus to pave the way for the use of resource-efficient constructions with a high level of design freedom. 3D Concrete Printing. BibRef

Ma, X.Z.[Xin-Zhu], Liu, S.N.[Shi-Nan], Xia, Z.Y.[Zhi-Yi], Zhang, H.W.[Hong-Wen], Zeng, X.Y.[Xing-Yu], Ouyang, W.L.[Wan-Li],
Rethinking Pseudo-Lidar Representation,
ECCV20(XIII:311-327).
Springer DOI 2011
the efficacy of pseudo-LiDAR representation comes from the coordinate transformation, instead of data representation BibRef

Yang, Z.[Ze], Xu, Y.H.[Ying-Hao], Xue, H.[Han], Zhang, Z.[Zheng], Urtasun, R.[Raquel], Wang, L.W.[Li-Wei], Lin, S.[Stephen], Hu, H.[Han],
Dense Reppoints: Representing Visual Objects with Dense Point Sets,
ECCV20(XXI:227-244).
Springer DOI 2011
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Cosmo, L.[Luca], Norelli, A.[Antonio], Halimi, O.[Oshri], Kimmel, R.[Ron], Rodolà, E.[Emanuele],
LIMP: Learning Latent Shape Representations with Metric Preservation Priors,
ECCV20(III:19-35).
Springer DOI 2012
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Cosmo, L.[Luca], Minello, G.[Giorgia], Bronstein, M.M.[Michael M.], Rossi, L.[Luca], Torsello, A.[Andrea],
The Average Mixing Kernel Signature,
ECCV20(XX:1-17).
Springer DOI 2011
signature for points on non-rigid three-dimensional shapes. BibRef

Yu, R.X.[Rui-Xuan], Wei, X.[Xin], Tombari, F.[Federico], Sun, J.[Jian],
Deep Positional and Relational Feature Learning for Rotation-invariant Point Cloud Analysis,
ECCV20(X:217-233).
Springer DOI 2011
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Sheshappanavar, S.V.[Shivanand Venkanna], Kambhamettu, C.[Chandra],
A Novel Local Geometry Capture in Pointnet++ for 3D Classification,
DLGC20(1059-1068)
IEEE DOI 2008
Ellipsoids, Solid modeling, Feature extraction, Shape, Computational modeling BibRef

Guan, Y.[Yanran], Jahan, T.[Tansin], van Kaick, O.[Oliver],
Generalized Autoencoder for Volumetric Shape Generation,
L3DGM20(1082-1088)
IEEE DOI 2008
Shape, Training, Solid modeling, Manifolds, Interpolation, Decoding BibRef

Chen, N.L.[Neng-Lun], Liu, L.J.[Ling-Jie], Cui, Z.M.[Zhi-Ming], Chen, R.N.[Run-Nan], Ceylan, D.G.[Duy-Gu], Tu, C.H.[Chang-He], Wang, W.P.[Wen-Ping],
Unsupervised Learning of Intrinsic Structural Representation Points,
CVPR20(9118-9127)
IEEE DOI 2008
Shape, Task analysis, Feature extraction, Semantics, Principal component analysis, Machine learning BibRef

Mo, K.C.[Kai-Chun], Guerrero, P.[Paul], Yi, L.[Li], Su, H.[Hao], Wonka, P.[Peter], Mitra, N.J.[Niloy J.], Guibas, L.J.[Leonidas J.],
StructEdit: Learning Structural Shape Variations,
CVPR20(8856-8865)
IEEE DOI 2008
Coding differences in shapes. Shape, Geometry, Decoding, Encoding BibRef

Hao, Z.K.[Ze-Kun], Averbuch-Elor, H.[Hadar], Snavely, N.[Noah], Belongie, S.[Serge],
DualSDF: Semantic Shape Manipulation Using a Two-Level Representation,
CVPR20(7628-7638)
IEEE DOI 2008
Shape, Neural networks, Solid modeling, Surface reconstruction, Automobiles, Couplings BibRef

Nunez, E., Joshi, S.H.,
Deep Learning of Warping Functions for Shape Analysis,
Diff-CVML20(3782-3790)
IEEE DOI 2008
Shape, Machine learning, Computational efficiency, Dynamic programming, Training, Prediction algorithms, Sociology BibRef

Mezghanni, M.[Mariem], Bodrito, T.[Théo], Boulkenafed, M.[Malika], Ovsjanikov, M.[Maks],
Physical Simulation Layer for Accurate 3D Modeling,
CVPR22(13504-13513)
IEEE DOI 2210
Training, Visualization, Solid modeling, Shape, Computational modeling, Programming, Vision + graphics BibRef

Gadelha, M., Gori, G., Ceylan, D., Mech, R., Carr, N., Boubekeur, T., Wang, R., Maji, S.,
Learning Generative Models of Shape Handles,
CVPR20(399-408)
IEEE DOI 2008
Shape, Task analysis, Solid modeling, Interpolation, Silicon, Computational modeling BibRef

Zhu, J., Fang, Y.,
Reference Grid-assisted Network for 3D Point Signature Learning from Point Clouds,
WACV20(211-220)
IEEE DOI 2006
Feature extraction, Robustness, Task analysis, Geometry BibRef

Avants, B.[Brian], Greenblatt, E.[Elliot], Hesterman, J.[Jacob], Tustison, N.[Nicholas],
Deep Volumetric Feature Encoding for Biomedical Images,
WBIR20(91-100).
Springer DOI 2006
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Achlioptas, P., Guibas, L.J.[Leonidas J.], Goodman, N., Fan, J., Hawkins, R.,
Shapeglot: Learning Language for Shape Differentiation,
ICCV19(8937-8946)
IEEE DOI 2004
CAD, image representation, learning (artificial intelligence), natural languages, object recognition, Pragmatics BibRef

Kulkarni, N., Tulsiani, S., Gupta, A.,
Canonical Surface Mapping via Geometric Cycle Consistency,
ICCV19(2202-2211)
IEEE DOI 2004
object detection, solid modelling, supervised learning, 3D model, extensive manual labeling, Solid modeling BibRef

Littwin, G., Wolf, L.B.,
Deep Meta Functionals for Shape Representation,
ICCV19(1824-1833)
IEEE DOI 2004
Code, 3D.
WWW Link. image classification, image reconstruction, image representation, image resolution, neural nets, shape recognition, BibRef

Cosmo, L.[Luca], Panine, M.[Mikhail], Rampini, A.[Arianna], Ovsjanikov, M.[Maks], Bronstein, M.M.[Michael M.], Rodola, E.[Emanuele],
Isospectralization, or How to Hear Shape, Style, and Correspondence,
CVPR19(7521-7530).
IEEE DOI 2002
Can one recover the shape of a geometric object from its Laplacian spectrum? BibRef

Shen, W.C.[Wei-Chao], Jia, Y.D.[Yun-De], Wu, Y.W.[Yu-Wei],
3D Shape Reconstruction From Images in the Frequency Domain,
CVPR19(4466-4474).
IEEE DOI 2002
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Yin, K., Chen, Z., Chaudhuri, S., Fisher, M., Kim, V.G., Zhang, H.,
COALESCE: Component Assembly by Learning to Synthesize Connections,
3DV20(61-70)
IEEE DOI 2102
Shape, Geometry, Surface reconstruction, Solid modeling, Transforms, Joining processes BibRef

Muralikrishnan, S.[Sanjeev], Kim, V.G.[Vladimir G.], Fisher, M.[Matthew], Chaudhuri, S.[Siddhartha],
Shape Unicode: A Unified Shape Representation,
CVPR19(3785-3794).
IEEE DOI 2002
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Park, J.J.[Jeong Joon], Florence, P.[Peter], Straub, J.[Julian], Newcombe, R.A.[Richard A.], Lovegrove, S.J.[Steven J.],
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation,
CVPR19(165-174).
IEEE DOI 2002
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Ambellan, F.[Felix], Zachow, S.[Stefan], von Tycowicz, C.[Christoph],
An As-invariant-as-possible Gl+(3)-based Statistical Shape Model,
MFCA19(219-228).
Springer DOI 1912
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Jallouli, M.[Malika], Khalifa, W.B.[Wafa Belhadj], Ben Mabrouk, A.[Anouar], Mahjoub, M.A.[Mohamed Ali],
Toward New Spherical Harmonic Shannon Entropy for Surface Modeling,
CAIP19(II:38-48).
Springer DOI 1909
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Simpson, I.J.A.[Ivor J.A.], Vicente, S.[Sara], Campbell, N.D.F.[Neill D.F.],
Learning Structured Gaussians to Approximate Deep Ensembles,
CVPR22(366-374)
IEEE DOI 2210
Solid modeling, Uncertainty, Correlation, Computational modeling, Estimation, Predictive models, Statistical methods, 3D from single images BibRef

di Martino, A.[Alessandro], Bodin, E.[Erik], Ek, C.H.[Carl Henrik], Campbell, N.D.F.[Neill D. F.],
Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation,
ACCV18(IV:3-20).
Springer DOI 1906
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Piewak, F.[Florian], Pinggera, P.[Peter], Enzweiler, M.[Markus], Pfeiffer, D.[David], Zöllner, M.[Marius],
Improved Semantic Stixels via Multimodal Sensor Fusion,
GCPR18(447-458).
Springer DOI 1905
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Liu, S., Giles, C.L., Ororbia, A.G.,
Learning a Hierarchical Latent-Variable Model of 3D Shapes,
3DV18(542-551)
IEEE DOI 1812
image reconstruction, image representation, image retrieval, learning (artificial intelligence), probability, solid modelling, image reconstruction BibRef

Kuse, M., Jaiswal, S.P., Shen, S.,
Deep-mapnets: A residual network for 3D environment representation,
ICIP17(2652-2656)
IEEE DOI 1803
Cameras, Data models, Rendering (computer graphics), Solid modeling, Training, Transforms, residual network learning BibRef

Zhao, M., Cheung, G., Florencio, D., Ji, X.,
Progressive graph-signal sampling and encoding for static 3D geometry representation,
ICIP17(735-739)
IEEE DOI 1803
Encoding, Geometry, Image coding, Kernel, Manifolds, progressive coding BibRef

Das, S., Bhandarkar, S.M.,
Local Geometry Inclusive Global Shape Representation,
MADO17(1256-1265)
IEEE DOI 1802
Computational modeling, Geometry, Measurement, Optimization, Shape, Strain, Three-dimensional displays, 3D shape representation, shape symmetry BibRef

Zou, C., Yumer, E., Yang, J., Ceylan, D., Hoiem, D.[Derek],
3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks,
ICCV17(900-909)
IEEE DOI 1802
Gaussian processes, image representation, recurrent neural nets, 3D world, 3DPRNN, Gaussian fields, abstract shape representation, Training BibRef

Elhabian, S.[Shireen], Whitaker, R.[Ross],
ShapeOdds: Variational Bayesian Learning of Generative Shape Models,
CVPR17(2185-2196)
IEEE DOI 1711
Bayes methods, Computational modeling, Data models, Image segmentation, Load modeling, Shape, Training BibRef

Sinha, A.[Ayan], Unmesh, A.[Asim], Huang, Q.X.[Qi-Xing], Ramani, K.[Karthik],
SurfNet: Generating 3D Shape Surfaces Using Deep Residual Networks,
CVPR17(791-800)
IEEE DOI 1711
Atmospheric modeling, Geometry, Neural networks, Shape, Solid modeling, Surface reconstruction, BibRef

Toure, E.H.B., Fall, I., Bah, A., Camara, M.S., Ba, M.,
Consistency preserving for evolving megamodels through axiomatic semantics,
ISCV17(1-8)
IEEE DOI 1710
Data models, Software, Unified modeling language, Axiomatic Semantics, Megamodels, Software Evolution. BibRef

Saleem, N.H.[Noor Haitham], Chien, H.J.[Hsiang-Jen], Rezaei, M.[Mahdi], Klette, R.[Reinhard],
Improved Stixel Estimation Based on Transitivity Analysis in Disparity Space,
CAIP17(I: 28-40).
Springer DOI 1708
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Abbeloos, W.[Wim], Goedemé, T.[Toon],
Point Pair Feature Based Object Detection for Random Bin Picking,
CRV16(432-439)
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Bin Picking. Representation for free form 3D. BibRef

Firman, M.[Michael],
RGBD Datasets: Past, Present and Future,
LS3D16(661-673)
IEEE DOI 1612
Survey, Datasets. reviewing datasets across eight categories: semantics, object pose estimation, camera tracking, scene reconstruction, object tracking, human actions, faces and identification. BibRef

Pramod, R.T., Arun, S.P.,
Do Computational Models Differ Systematically from Human Object Perception?,
CVPR16(1601-1609)
IEEE DOI 1612
What does the representation do regarding recognition. BibRef

Akizuki, S.[Shuichi], Hashimoto, M.[Manabu],
Physical Reasoning for 3D Object Recognition Using Global Hypothesis Verification,
6DPose16(III: 595-605).
Springer DOI 1611
Can the layout in a scene hypothesis be achieved by using simple collision detection. BibRef

Krcál, M.[Marek], Pilarczyk, P.[Pawel],
Computation of Cubical Steenrod Squares,
CTIC16(140-151).
Springer DOI 1608
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Lienhardt, P.[Pascal], Peltier, S.[Samuel],
Homology Computation During an Incremental Construction Process,
CTIC16(7-15).
Springer DOI 1608
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Su, H.[Hang], Maji, S.[Subhransu], Kalogerakis, E.[Evangelos], Learned-Miller, E.G.[Erik G.],
Multi-view Convolutional Neural Networks for 3D Shape Recognition,
ICCV15(945-953)
IEEE DOI 1602
Cameras BibRef

George, D.[David], Tam, G.[Gary], Xie, X.H.[Xiang-Hua],
Analysis of face and segment level descriptors for robust 3D co-segmentation,
BMVW15(xx-yy).
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Kimmel, R.[Ron],
A Spectral Perspective on Shapes,
BMVC15(xx-yy).
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Cuesta, J.J.[Jhouben J.], Álvarez, M.A.[Mauricio A.], Orozco, Á.Á.[Álvaro Á.],
Global and Local Gaussian Process for Multioutput and Treed Data,
CIAP15(I:161-171).
Springer DOI 1511
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Wu, Z.R.[Zhi-Rong], Song, S.[Shuran], Khosla, A.[Aditya], Yu, F.[Fisher], Zhang, L.[Linguang], Tang, X.[Xiaoou], Xiao, J.X.[Jian-Xiong],
3D ShapeNets: A deep representation for volumetric shapes,
CVPR15(1912-1920)
IEEE DOI 1510
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Godehardt, M.[Michael], Jablonski, A.[Andreas], Wirjadi, O.[Oliver], Schladitz, K.[Katja],
Fast Estimation of Intrinsic Volumes in 3D Gray Value Images,
ISMM15(657-668).
Springer DOI 1506
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Opgenhaffen, L., Sepers, M.H.,
3D Modelling: Crossing Traditional Boundaries Between Different Research Areas,
3D-Arch15(411-414).
DOI Link 1504
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Kramarev, V.[Vladislav], Walas, K.[Krzysztof], Leonardis, A.[Aleš],
Categorisation of 3D Objects in Range Images Using Compositional Hierarchies of Parts Based on MDL and Entropy Selection Criteria,
SCIA15(289-301).
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Kramarev, V.[Vladislav], Zurek, S.[Sebastian], Wyatt, J.L.[Jeremy L.], Leonardis, A.[Ales],
Object Categorization from Range Images Using a Hierarchical Compositional Representation,
ICPR14(586-591)
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Feature extraction. hierarchical compositional representation of 3D shape. BibRef

Uhrig, J.[Jonas], Cordts, M.[Marius], Franke, U.[Uwe], Brox, T.[Thomas],
Pixel-Level Encoding and Depth Layering for Instance-Level Semantic Labeling,
GCPR16(14-25).
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Award, GCPR, HM. BibRef

Cordts, M.[Marius], Schneider, L.[Lukas], Enzweiler, M.[Markus], Franke, U.[Uwe], Roth, S.[Stefan],
Object-Level Priors for Stixel Generation,
GCPR14(172-183).
Springer DOI 1411
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Grbic, S.[Saša], Swee, J.K.Y.[Joshua K.Y.], Ionasec, R.I.[Razvan I.],
ShapeForest: Building Constrained Statistical Shape Models with Decision Trees,
ECCV14(III: 597-612).
Springer DOI 1408
locate points on deformable objects. BibRef

Scharwächter, T.[Timo], Enzweiler, M.[Markus], Franke, U.[Uwe], Roth, S.[Stefan],
Stixmantics: A Medium-Level Model for Real-Time Semantic Scene Understanding,
ECCV14(V: 533-548).
Springer DOI 1408
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Lee, J.Y.[Joon-Young], Jung, J.Y.[Ji-Young], Bok, Y.[Yunsu], Park, J.[Jaesik], Choi, D.G.[Dong-Geol], Han, Y.[Yudeog], Kweon, I.S.[In So],
Robust Computer Vision Techniques for High-Quality 3D Modeling,
ACPR13(6-10)
IEEE DOI 1408
computer vision BibRef

Liu, Z.B.[Zhen-Bao], Zhang, F.[Feng], Bu, S.H.[Shu-Hui],
Spectral Classification of 3D Articulated Shapes,
MMMod14(II: 315-322).
Springer DOI 1405
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Rao, Q.[Qing], Grünler, C.[Christian], Hammori, M.[Markus], Chakraborty, S.[Samarjit],
Stixel on the Bus: An Efficient Lossless Compression Scheme for Depth Information in Traffic Scenarios,
MMMod14(I: 568-579).
Springer DOI 1405
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Urschler, M., Bornik, A., Donoser, M.,
Memory Efficient 3D Integral Volumes,
BD3DCV13(722-729)
IEEE DOI 1403
computer vision BibRef

Fabado, S., Seguí, A.E., Cabrelles, M., Navarro, S., García-De-San-Miguel, D., Lerma, J.L.,
3DVEM Software Modules for Efficient Management of Point Clouds and Photorealistic 3D Models,
CIPA13(255-260).
DOI Link 1311
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Wegrzyn, D.[Dominik], Alexandre, L.A.[Luís A.],
A Genetic Algorithm-Evolved 3D Point Cloud Descriptor,
CIARP13(I:92-99).
Springer DOI 1311
Descriptor for 3D point clouds. BibRef

Cipolla, R.,
3D shape and its applications,
ICARCV12(11).
IEEE DOI 1304
State of the art review of shape: registration, reconstruction and recognition. BibRef

Wang, P.[Ping], Wang, W.[Wei], Gao, Y.H.[Ying-Hui], Li, S.F.[Shi-Fei],
A recognition approach of 3-D objects based on the Tsallis entropy,
CVRS12(242-245).
IEEE DOI 1302
Spin images related. 3D surface descriptions. BibRef

Veltkamp, R.C.[Remco C.], ter Haar, F.B.[Frank B.],
SHREC 2009: Shape Retrieval Contest,
3DOR09(57-59)
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Bronstein, A.M., Bronstein, M.M., Castellani, U., Falcidieno, B., Fusiello, A., Godil, A., Guibas, L.J., Kokkinos, I., Lian, Z., Ovsjanikov, M., Patané, G., Spagnuolo, M., Toldo, R.,
SHREC'10 Track: Robust Shape Retrieval,
EG3DOR10(71-78)
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Myrcha, J.[Julian], Rokita, P.[Przemyslaw],
Multimedia Objects Conversion for a Digital Repository: A Case Study,
ICCVG12(196-203).
Springer DOI 1210
Format conversion. BibRef

Dezhkam, A.[Arsalan], Kasaei, S.[Shohreh],
A fast 3D reconstruction of inextensible surfaces using adaptive weights,
ICIIP11(1-7).
IEEE DOI 1112
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Sturm, P.F.[Peter F.],
A Historical Survey of Geometric Computer Vision,
CAIP11(I: 1-8).
Springer DOI 1109
Survey, Geometric. BibRef

Terashima, Y.[Yoshito],
Scene Classification with a Biologically Inspired Method,
CSAIL(TR-2009-020). 2009-05-10
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Sharma, A.[Avinash], Horaud, R.[Radu],
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NORDIA10(29-36).
IEEE DOI 1006
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Shinagawa, Y.[Yoshihisa], Lin, Y.P.[Yu-Ping],
Untangling fibers by quotient appearance manifold mapping for grayscale shape classification,
ICCV09(2006-2013).
IEEE DOI 0909
Fibers in appearance manifold of shape classes in Euclidean space. BibRef

Wang, B.[Bing], Rushmeier, H.E.[Holly E.],
A compact representation for scanned 3D objects,
3DIM09(1793-1800).
IEEE DOI 0910
Representation to preserve the look of the object (graphics) rather than precise measurements. BibRef

Zhong, Y.[Yu],
Intrinsic shape signatures: A shape descriptor for 3D object recognition,
3DRR09(689-696).
IEEE DOI 0910
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Pan, H.F.[Hong-Fei], Liang, D.[Don], Tang, J.[Jun], Wang, N.[Nian],
Shape Representation and Clustering Based on Laplacian Spectrum,
CISP09(1-4).
IEEE DOI 0910
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Colombe, J.B.,
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AIPR04(86-91).
IEEE DOI 0410
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Muffert, M.[Maximilian], Schneider, N.[Nicolai], Franke, U.[Uwe],
Stix-Fusion: A Probabilistic Stixel Integration Technique,
CRV14(16-23)
IEEE DOI 1406
Current measurement BibRef

Pfeiffer, D.[David], Erbs, F.[Friedrich], Franke, U.[Uwe],
Pixels, Stixels, and Objects,
CVVT12(III: 1-10).
Springer DOI 1210
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Pfeiffer, D.[David], Franke, U.[Uwe],
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BMVC11(xx-yy).
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Badino, H.[Hernán], Franke, U.[Uwe], Pfeiffer, D.[David],
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DAGM09(51-60).
Springer DOI 0909
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Atmosukarto, I.[Indriyati], Shapiro, L.G.[Linda G.],
A Learning Approach to 3D Object Representation for Classification,
SSPR08(267-276).
Springer DOI 0812
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Chung, K.L.[Kai Lun], Zhuo, W.[Wei],
Graph-Based Visual Analytic Tools for Parallel Coordinates,
ISVC08(II: 990-999).
Springer DOI 0812
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Duan, Q.S.[Qi-San], Chen, X.X.[Xue-Xia], Lü, W.X.[Wen-Xu],
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Xu, G.L.[Guo-Liang],
Finite Element Methods for Geometric Modeling and Processing Using General Fourth Order Geometric Flows,
GMP08(xx-yy).
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Shape Interrogation,
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Leng, B.[Biao], Li, L.Q.[Li-Qun], Qin, Z.[Zheng],
MADE: A Composite Visual-Based 3D Shape Descriptor,
MIRAGE07(93-104).
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See also Automatic Combination of Feature Descriptors for Effective 3D Shape Retrieval. BibRef

Dai, J.F.[Jun-Fei], Luo, W.[Wei], Yau, S.T.[Shing-Tung], Gu, X.F.D.[Xian-Feng David],
Geometric Accuracy Analysis for Discrete Surface Approximation,
GMP06(59-72).
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Mio, W., Liu, X.W.[Xiu-Wen],
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ICIP06(2113-2116).
IEEE DOI 0610
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Ha, V.H.S., Moura, J.M.F.,
Three-dimensional intrinsic shape,
ICIP04(III: 2099-2102).
IEEE DOI 0505

See also Efficient 2D shape orientation. BibRef

Chao, J.H.[Jin-Hui], Li, F.X.[Fang Xing],
A Surface Model Based on a Fibre Bundle of 1-Parameter Groups of Hamiltonian Lie Algebra,
ICIP05(I: 1021-1024).
IEEE DOI 0512
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Chao, J.H.[Jin-Hui], Kim, J.D.[Jong-Dae],
A fibre bundle model of surfaces and its generalization,
ICPR04(I: 560-563).
IEEE DOI 0409
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Vranic, D.V.,
An improvement of rotation invariant 3D-shape descriptor based on functions on concentric spheres,
ICIP03(III: 757-760).
IEEE DOI 0312
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Kimmel, R.,
Geometric segmentation of 3D structures,
ICIP03(II: 639-642).
IEEE DOI 0312
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Koenderink, J.J.[Jan J.],
Monocentric Optical Space,
CAIP03(689-696).
Springer DOI 0311
Visual space is a homogeneous, flat non-Euclidean space. Ambiguities in the 2-D view.
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Wahl, E., Hillenbrand, U.[Ulrich], Hirzinger, G.[Gerd],
Surflet-Pair-Relation Histograms: A Statistical 3D-Shape Representation for Rapid Classification,
3DIM03(474-481).
IEEE DOI 0311
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Liu, X.G.[Xin-Guo], Sun, R., Kang, S.B.[Sing Bing], Shum, H.Y.[Heung-Yeung],
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CVPR03(I: 813-820).
IEEE DOI 0307
captures the shape variation of an object. first extract a directional distribution of thickness histogram signatures. BibRef

Torralba, A.B.[Antonio B.], Freeman, W.T.[William T.],
Properties and Applications of Shape Recipes,
CVPR03(II: 383-390).
IEEE DOI 0307
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WWW Link. In low-level vision, the representation of scene properties such as shape, albedo, etc., are very high dimensional as they have to describe complicated structures. The approach proposed here is to let the image itself bear as much of the representational burden as possible. 0306
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Freeman, W.T.[William T.], Torralba, A.B.[Antonio B.],
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Watanabe, T.[Toshinori], Sugawara, K.[Ken],
Ill-Configured Object Representation by Neighbour Set with Applications to Aerial Image Analyses,
PCV02(A: 387). 0305
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Sinha, P.[Pawan],
Qualitative Representations for Recognition,
BMCV02(249 ff.).
Springer DOI 0303
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Lin, Q.F.[Qing-Fen],
Enhancement, Detection, and Visualization of 3D Volume Data,
Ph.D.Thesis, Linkoping University, May, 2003.
HTML Version. BibRef 0305

Danielsson, P.E.[Per-Erik], Lin, Q.[Qingfen],
A New Shape Space for Second Order 3D-Variations,
VF01(145 ff.).
Springer DOI 0209
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Pollefeys, M.[Marc], Kang, S.B.[Sing Bing], Slabaugh, G.G.[Gregory G.], Cornelis, K.[Kurt], Debevec, P.E.[Paul E.],
Panel Session on Visual Scene Representation,
SMILE00(161 ff.).
Springer DOI 0209
BibRef

Triggs, B.[Bill], Nistér, D.[David], Kanatani, K.[Kenichi], Ponce, J.[Jean], Zhang, Z.Y.[Zheng-You],
Panel Session on Computations and Algorithms,
SMILE00(86 ff.).
Springer DOI 0209
BibRef

Hlavac, V.,
3D reconstruction, omnidirectional vision and understanding of scenes,
BMVC02(Invited Talk). 0208
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Reed, T.,
The Representation and Coding of Volumetric Images Using the 3-d Derivative of Gaussian Transform,
ICIP01(II: 585-588).
IEEE DOI 0108
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Lamboray, E., Wumlin, S., Waschbusch, M., Gross, M., Pfister, H.,
Unconstrained free-viewpoint video coding,
ICIP04(V: 3261-3264).
IEEE DOI 0505
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Pfister, H., Zwicker, M., van Baar, J.[Jeroen], Gross, M.,
Surfels: Surface Elements as Rendering Primitives,
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Point samples for efficient 3D processing and content creation,
3DPVT04(742-742).
IEEE DOI 0412
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Sramek, M., Dimitrov, L.I.,
f3d: A file format and tools for storage and manipulation of volumetric data sets,
3DPVT02(368-371). 0206
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Bilodeau, G.A.[Guillaume-Alexandre], Bergevin, R.[Robert],
Generic Modeling of 3d Objects from Single 2d Images,
ICPR00(Vol I: 770-773).
IEEE DOI 0009
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Knutsson, H., Andersson, M., Borga, M., Wiklund, J.,
Automated Generation of Representations in Vision,
ICPR00(Vol III: 59-66).
IEEE DOI 0009
Survey. BibRef

Borga, M., Andersson, M., Knutsson, H.,
Generation of Representations for Supervised Learning: A Velocity Estimation Example,
SCIA01(O-W2). 0206
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Earlier: A3, A1, Only:
Learning visual operators from examples: a new paradigm in image processing,
CIAP99(58-62).
IEEE DOI 9909
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Mohr, R., Buschmann, R., Falkenhagen, L., Van Gool, L.J., Koch, R.,
CUMULI, PANORAMA, and VANGUARD Project Overview,
SMILE98(xx-yy). BibRef 9800

Johnson, A.E., Hoffman, R., Osborn, J., Hebert, M.,
A System for Semi-Automated Modelling of Complex Environments,
3DIM97(9 - Environment Modeling) 9702
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Chown, T.J., Lewis, P.H.,
EFRM-based detection and extraction of ridge and valley features in grey level images,
ICPR92(III:339-342).
IEEE DOI 9208
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Jia, Y.D.[Yun-De],
Description and recognition of curved objects,
ICPR92(III:464-467).
IEEE DOI 9208
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Abe, N., Itho, F., and Tsuji, S.,
Toward Generation of 3-Dimensional Models of Objects Using 2-Dimensional Figures and Explanations in Language,
IJCAI83(1113-1115). BibRef 8300

Baker, H.H.,
Three-Dimensional Modelling,
IJCAI77(649-655). BibRef 7700

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Generation or Representation of Surface Patches .


Last update:Mar 16, 2024 at 20:36:19