11.2.4.4 Range and Color, RGB-D Segmentation and Analysis

Hydra:,

WWW Link. Real-time system to build 3D scene graphs from sensor data. Code, Scene Graph. Code, RGB-D.

Hulik, R.[Rostislav], Spanel, M.[Michal], Smrz, P.[Pavel], Materna, Z.[Zdenek],
Continuous plane detection in point-cloud data based on 3D Hough Transform,
JVCIR(25), No. 1, 2014, pp. 86-97.
Elsevier DOI 1502
Hough Transform. RGB-D sensor BibRef

Stückler, J.[Jörg], Behnke, S.[Sven],
Multi-resolution surfel maps for efficient dense 3D modeling and tracking,
JVCIR(25), No. 1, 2014, pp. 137-147.
Elsevier DOI 1502
BibRef
And: Corrigendum: JVCIR(26), No. 1, 2015, pp. 349-.
Elsevier DOI 1502
3D multi-resolution RGB-D image representation BibRef

Riaz, Z.[Zahid], Linder, T.[Thorsten], Behnke, S.[Sven], Worst, R.[Rainer], Surmann, H.[Hartmut],
Efficient Transmission and Rendering of RGB-D Views,
ISVC13(I:517-526).
Springer DOI 1310
BibRef

Liu, H.[Haowei], Philipose, M.[Matthai], Sun, M.T.[Ming-Ting],
Automatic objects segmentation with RGB-D cameras,
JVCIR(25), No. 4, 2014, pp. 709-718.
Elsevier DOI 1403
BibRef
Earlier:
Automatic object segmentation with 3-D cameras,
ICIP12(569-572).
IEEE DOI 1302
Boundary detection
See also Recognizing object manipulation activities using depth and visual cues. BibRef

He, B.[Bei], Wang, G.J.[Gui-Jin], Zhang, C.[Cha],
Iterative transductive learning for automatic image segmentation and matting with RGB-D data,
JVCIR(25), No. 5, 2014, pp. 1031-1043.
Elsevier DOI 1406
Image matting BibRef

Camplani, M.[Massimo], del Blanco, C.R.[Carlos Roberto], Salgado, L.[Luis], Jaureguizar, F.[Fernando], García, N.[Narciso],
Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction,
MVA(25), No. 5, July 2014, pp. 1197-1210.
Springer DOI 1407
BibRef

Camplani, M.[Massimo], Salgado, L.[Luis],
Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers,
JVCIR(25), No. 1, 2014, pp. 122-136.
Elsevier DOI 1502
RGB-D cameras BibRef

Camplani, M.[Massimo], del Blanco, C.R.[Carlos R.], Salgado, L.[Luis], Jaureguizar, F.[Fernando], García, N.[Narciso],
Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers,
PRL(50), No. 1, 2014, pp. 23-33.
Elsevier DOI 1410
Region-based background modeling BibRef

Husain, F., Dellen, B., Torras, C.,
Consistent Depth Video Segmentation Using Adaptive Surface Models,
Cyber(45), No. 2, February 2015, pp. 266-278.
IEEE DOI 1502
image segmentation BibRef

Abramov, A.[Alexey], Pauwels, K.[Karl], Papon, J.[Jeremie], Worgotter, F.[Florentin], Dellen, B.[Babette],
Depth-supported real-time video segmentation with the Kinect,
WACV12(457-464).
IEEE DOI 1203
Depth aided. BibRef

Yang, J., Gan, Z., Li, K., Hou, C.,
Graph-Based Segmentation for RGB-D Data Using 3-D Geometry Enhanced Superpixels,
Cyber(45), No. 5, May 2015, pp. 913-926.
IEEE DOI 1505
Color BibRef

Wang, A.[Anran], Lu, J.W.[Ji-Wen], Cai, J.F.[Jian-Fei], Wang, G.[Gang], Cham, T.J.[Tat-Jen],
Unsupervised Joint Feature Learning and Encoding for RGB-D Scene Labeling,
IP(24), No. 11, November 2015, pp. 4459-4473.
IEEE DOI 1509
BibRef
Earlier: A1, A2, A4, A3, A5:
Multi-modal Unsupervised Feature Learning for RGB-D Scene Labeling,
ECCV14(V: 453-467).
Springer DOI 1408
image coding BibRef

Wang, A.[Anran], Cai, J.F.[Jian-Fei], Lu, J.W.[Ji-Wen], Cham, T.J.[Tat-Jen],
Modality and Component Aware Feature Fusion for RGB-D Scene Classification,
CVPR16(5995-6004)
IEEE DOI 1612
BibRef
Earlier:
MMSS: Multi-modal Sharable and Specific Feature Learning for RGB-D Object Recognition,
ICCV15(1125-1133)
IEEE DOI 1602
Computer vision BibRef

Wang, A.[Anran], Lu, J.W.[Ji-Wen], Cai, J.F.[Jian-Fei], Cham, T.J.[Tat-Jen], Wang, G.[Gang],
Large-Margin Multi-Modal Deep Learning for RGB-D Object Recognition,
MultMed(17), No. 11, November 2015, pp. 1887-1898.
IEEE DOI 1511
Correlation BibRef

Tang, J., Jin, L., Li, Z., Gao, S.,
RGB-D Object Recognition via Incorporating Latent Data Structure and Prior Knowledge,
MultMed(17), No. 11, November 2015, pp. 1899-1908.
IEEE DOI 1511
Data structures BibRef

Beksi, W.J.[William J.], Papanikolopoulos, N.[Nikolaos],
A topology-based descriptor for 3D point cloud modeling: Theory and experiments,
IVC(88), 2019, pp. 84-95.
Elsevier DOI 1908
Topological data analysis, Persistent homology, Shape analysis, Object classification BibRef

Arshad, M.S.[Mohammad Samiul], Beksi, W.J.[William J.],
IPVNet: Learning implicit point-voxel features for open-surface 3D reconstruction,
JVCIR(97), 2023, pp. 103970.
Elsevier DOI 2312
BibRef
Earlier:
A Progressive Conditional Generative Adversarial Network for Generating Dense and Colored 3D Point Clouds,
3DV20(712-722)
IEEE DOI 2102
3D reconstruction, Open surfaces, Implicit functions. Image color analysis, Generative adversarial networks, Geometry, Training, Generators, Shape BibRef

Stückler, J.[Jörg], Waldvogel, B.[Benedikt], Schulz, H.[Hannes], Behnke, S.[Sven],
Dense real-time mapping of object-class semantics from RGB-D video,
RealTimeIP(10), No. 4, December 2015, pp. 599-609.
Springer DOI 1512
BibRef

Sanchez-Riera, J.[Jordi], Hua, K.L.[Kai-Lung], Hsiao, Y.S.[Yuan-Sheng], Lim, T.[Tekoing], Hidayati, S.C.[Shintami C.], Cheng, W.H.[Wen-Huang],
A comparative study of data fusion for RGB-D based visual recognition,
PRL(73), No. 1, 2016, pp. 1-6.
Elsevier DOI 1604
RGB-D BibRef

Ahmed, N.[Naveed], Khalifa, S.[Salam],
Time-coherent 3D animation reconstruction from RGB-D video,
SIViP(10), No. 4, April 2016, pp. 783-790.
Springer DOI 1604
BibRef

Thøgersen, M.[Mikkel], Escalera, S.[Sergio], Gonzàlez, J.[Jordi], Moeslund, T.B.[Thomas B.],
Segmentation of RGB-D indoor scenes by stacking random forests and conditional random fields,
PRL(80), No. 1, 2016, pp. 208-215.
Elsevier DOI 1609
RGB-D sematic segmentation BibRef

Hasnat, M.A.[M. Abul], Alata, O.[Olivier], Trémeau, A.[Alain],
Joint Color-Spatial-Directional Clustering and Region Merging (JCSD-RM) for Unsupervised RGB-D Image Segmentation,
PAMI(38), No. 11, November 2016, pp. 2255-2268.
IEEE DOI 1610
BibRef
Earlier:
Unsupervised Clustering of Depth Images Using Watson Mixture Model,
ICPR14(214-219)
IEEE DOI 1412
BibRef
Earlier:
Unsupervised RGB-D image segmentation using joint clustering and region merging,
BMVC14(xx-yy).
HTML Version. 1410
Clustering methods BibRef

Li, X.[Xiao], Fang, M.[Min], Zhang, J.J.[Ju-Jie], Wu, J.Q.[Jin-Qiao],
Learning Coupled Classifiers with RGB images for RGB-D object recognition,
PR(61), No. 1, 2017, pp. 433-446.
Elsevier DOI 1705
Object recognition BibRef

Ibañez, R.[Rodrigo], Soria, Á.[Álvaro], Teyseyre, A.[Alfredo], Rodríguez, G.[Guillermo], Campo, M.[Marcelo],
Approximate string matching: A lightweight approach to recognize gestures with Kinect,
PR(62), No. 1, 2017, pp. 73-86.
Elsevier DOI 1705
Natural user interfaces BibRef

Deng, Z.[Zhuo], Todorovic, S.[Sinisa], Latecki, L.J.[Longin Jan],
Unsupervised object region proposals for RGB-D indoor scenes,
CVIU(154), No. 1, 2017, pp. 127-136.
Elsevier DOI 1612
BibRef
Earlier: A1, A3, Only:
Unsupervised Segmentation of RGB-D Images,
ACCV14(III: 423-435).
Springer DOI 1504
Object segmentation BibRef

Zhao, L.J.[Li-Jun], Bai, H.H.[Hui-Hui], Wang, A.H.[An-Hong], Zhao, Y.[Yao], Zeng, B.[Bing],
Two-stage filtering of compressed depth images with Markov Random Field,
SP:IC(54), No. 1, 2017, pp. 11-22.
Elsevier DOI 1704
BibRef
Earlier: A1, A2, A3, A4, Only:
Joint iterative guidance filtering for compressed depth images,
VCIP16(1-4)
IEEE DOI 1701
MRF Color. Filter depth and color together. BibRef

Moyà-Alcover, G.[Gabriel], Elgammal, A.[Ahmed], Jaume-i-Capó, A.[Antoni], Varona, J.[Javier],
Modeling depth for nonparametric foreground segmentation using RGBD devices,
PRL(96), No. 1, 2017, pp. 76-85.
Elsevier DOI 1709
Background, subtraction BibRef

Trabelsi, R.[Rim], Jabri, I.[Issam], Smach, F.[Fethi], Bouallegue, A.[Ammar],
Efficient and fast multi-modal foreground-background segmentation using RGBD data,
PRL(97), No. 1, 2017, pp. 13-20.
Elsevier DOI 1709
Background, subtraction BibRef

Zheng, Y.B.[Ying-Bin], Ye, H.[Hao], Wang, L.[Li], Pu, J.[Jian],
Learning Multiviewpoint Context-Aware Representation for RGB-D Scene Classification,
SPLetters(25), No. 1, January 2018, pp. 30-34.
IEEE DOI 1801
feature extraction, image classification, image colour analysis, image representation, learning (artificial intelligence), scene classification BibRef

Tan, L.[Lu], Pan, Z.K.[Zhen-Kuan], Liu, W.Q.[Wan-Quan], Duan, J.M.[Jin-Ming], Wei, W.B.[Wei-Bo], Wang, G.D.[Guo-Dong],
Image Segmentation with Depth Information via Simplified Variational Level Set Formulation,
JMIV(60), No. 1, January 2018, pp. 1-17.
Springer DOI 1801
BibRef

Yu, Q.H.[Qing-Hua], Liang, J.[Jie], Xiao, J.H.[Jun-Hao], Lu, H.M.[Hui-Min], Zheng, Z.Q.[Zhi-Qiang],
A Novel perspective invariant feature transform for RGB-D images,
CVIU(167), 2018, pp. 109-120.
Elsevier DOI 1804
RGB-D images, Spatial invariant, Local visual feature BibRef

Cai, Z.Y.[Zi-Yun], Long, Y.[Yang], Shao, L.[Ling],
Adaptive RGB Image Recognition by Visual-Depth Embedding,
IP(27), No. 5, May 2018, pp. 2471-2483.
IEEE DOI 1804
Cameras, Image recognition, Kernel, Linear programming, Probability distribution, Task analysis, Training, RGB-D data, visual categorization BibRef

Cai, Z.Y.[Zi-Yun], Long, Y.[Yang], Jing, X.Y.[Xiao-Yuan], Shao, L.[Ling],
Adaptive Visual-Depth Fusion Transfer,
ACCV18(IV:56-73).
Springer DOI 1906
BibRef

Karpushin, M.[Maxim], Valenzise, G.[Giuseppe], Dufaux, F.[Frédéric],
TRISK: A local features extraction framework for texture-plus-depth content matching,
IVC(71), 2018, pp. 1-16.
Elsevier DOI 1804
Texture-plus-depth, RGBD, Local feature, Keypoint detector, Descriptor, Viewpoint changes BibRef

Junejo, I.N.[Imran N.], Ahmed, N.[Naveed],
Foreground extraction for freely moving RGBD cameras,
IET-CV(12), No. 3, April 2018, pp. 322-331.
DOI Link 1804
BibRef

Slavcheva, M.[Miroslava], Kehl, W.[Wadim], Navab, N.[Nassir], Ilic, S.[Slobodan],
SDF-2-SDF Registration for Real-Time 3D Reconstruction from RGB-D Data,
IJCV(126), No. 6, June 2018, pp. 615-636.
Springer DOI 1804
BibRef
Earlier:
SDF-2-SDF: Highly Accurate 3D Object Reconstruction,
ECCV16(I: 680-696).
Springer DOI 1611
3D from RGB-D BibRef

Slavcheva, M.[Miroslava], Ilic, S.[Slobodan],
SDF-TAR: Parallel Tracking and Refinement in RGB-D Data using Volumetric Registration,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Lu, F.X.[Fei-Xiang], Zhou, B.[Bin], Zhang, Y.[Yu], Zhao, Q.P.[Qin-Ping],
Real-time 3D scene reconstruction with dynamically moving object using a single depth camera,
VC(34), No. 6-8, June 2018, pp. 753-763.
Springer DOI 1806
Moving scanner. BibRef

Belter, D.[Dominik], Nowicki, M.[Michal], Skrzypczynski, P.[Piotr],
Modeling spatial uncertainty of point features in feature-based RGB-D SLAM,
MVA(29), No. 5, July 2018, pp. 827-844.
WWW Link. 1808
BibRef

Lin, Y.[Yangbin], Wang, C.[Cheng], Zhai, D.W.[Da-Wei], Li, W.[Wei], Li, J.[Jonathan],
Toward better boundary preserved supervoxel segmentation for 3D point clouds,
PandRS(143), 2018, pp. 39-47.
Elsevier DOI 1808
Supervoxel segmentation, Point clouds, Subset selection, Over-segmentation BibRef

Kang, Z.Z.[Zhi-Zhong], Yang, J.[Juntao],
A probabilistic graphical model for the classification of mobile LiDAR point clouds,
PandRS(143), 2018, pp. 108-123.
Elsevier DOI 1808
Mobile LiDAR, Probabilistic graphical model, Classification, Super-voxelization, Latent Dirichlet allocation BibRef

Li, M.[Minglei], Sun, C.M.[Chang-Ming],
Refinement of LiDAR point clouds using a super voxel based approach,
PandRS(143), 2018, pp. 213-221.
Elsevier DOI 1808
Point cloud, Octree, Super voxel, Data refinement BibRef

Jeong, S.H.[Seung-Hwa], Lee, J.J.[Jung-Jin], Kim, B.[Bumki], Kim, Y.[Young_Hui], Noh, J.Y.[Jun-Yong],
Object Segmentation Ensuring Consistency Across Multi-Viewpoint Images,
PAMI(40), No. 10, October 2018, pp. 2455-2468.
IEEE DOI 1809
Cameras, Image color analysis, Image segmentation, Object segmentation, Optimization, depth projection. RGB-D Camera. Shape from motion. BibRef

Teng, C.H.[Chin-Hung], Chuo, K.Y.[Kai-Yuan], Hsieh, C.Y.[Chen-Yuan],
Reconstructing three-dimensional models of objects using a Kinect sensor,
VC(34), No. 11, November 2018, pp. 1507-1523.
WWW Link. 1810
BibRef

Song, X., Jiang, S., Herranz, L., Chen, C.,
Learning Effective RGB-D Representations for Scene Recognition,
IP(28), No. 2, February 2019, pp. 980-993.
IEEE DOI 1811
Videos, Image recognition, Training, Tuning, Feature extraction, Databases, Data models, Scene recognition, deep learning, multimodal, RNN BibRef

Dai, J.T.[Ju-Ting], Tang, X.Y.[Xin-Yi],
ResFusion: deeply fused scene parsing network for RGB-D images,
IET-CV(12), No. 8, December 2018, pp. 1171-1178.
DOI Link 1812
BibRef

Zou, C.H.[Chu-Hang], Guo, R.Q.[Rui-Qi], Li, Z.Z.[Zhi-Zhong], Hoiem, D.[Derek],
Complete 3D Scene Parsing from an RGBD Image,
IJCV(127), No. 2, February 2019, pp. 143-162.
Springer DOI 1902
BibRef

Cheng, Y.H.[Yan-Hua], Zhao, X.[Xin], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
Semi-supervised learning and feature evaluation for RGB-D object recognition,
CVIU(139), No. 1, 2015, pp. 149-160.
Elsevier DOI 1509
BibRef
Earlier:
Semi-supervised Learning for RGB-D Object Recognition,
ICPR14(2377-2382)
IEEE DOI 1412
RGB-D Accuracy BibRef

Wang, D.[Dong], Yin, Q.Y.[Qi-Yue], He, R.[Ran], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Semi-supervised subspace segmentation,
ICIP14(2854-2858)
IEEE DOI 1502
Clustering algorithms BibRef

Li, Y.[Yabei], Zhang, Z.[Zhang], Cheng, Y.H.[Yan-Hua], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
MAPNet: Multi-modal attentive pooling network for RGB-D indoor scene classification,
PR(90), 2019, pp. 436-449.
Elsevier DOI 1903
BibRef
And: Corrigendum: PR(94), 2019, pp. 250.
Elsevier DOI 1906
Indoor scene classification, Multi-modal fusion, RGB-D, Attentive pooling BibRef

Malleson, C., Guillemaut, J.Y.[Jean-Yves], Hilton, A.,
Hybrid Modeling of Non-Rigid Scenes From RGBD Cameras,
CirSysVideo(29), No. 8, August 2019, pp. 2391-2404.
IEEE DOI 1908
Image reconstruction, Surface reconstruction, Shape, Cameras, Geometry, Dynamics, video plus depth BibRef

Siddiqua, A.[Ayesha], Fan, G.L.[Guo-Liang],
Semantics-enhanced supervised deep autoencoder for depth image-based 3D model retrieval,
PRL(125), 2019, pp. 806-812.
Elsevier DOI 1909
BibRef
Earlier:
Supervised Deep-Autoencoder for Depth Image-Based 3D Model Retrieval,
WACV18(939-946)
IEEE DOI 1806
3D model retrieval, Shape matching, Cross-modal retrieval, Deep autoencoder. feature extraction, image classification, image coding, image retrieval, image segmentation, Training BibRef

Schops, T.[Thomas], Sattler, T.[Torsten], Pollefeys, M.[Marc],
BAD SLAM: Bundle Adjusted Direct RGB-D SLAM,
CVPR19(134-144).
IEEE DOI 2002
BibRef

Xu, Z., Liu, S., Shi, J., Lu, C.,
Outdoor RGBD Instance Segmentation With Residual Regretting Learning,
IP(29), 2020, pp. 5301-5309.
IEEE DOI 2004
Image segmentation, Feature extraction, Proposals, Semantics, Robot sensing systems, Robustness, residual regretting BibRef

Chang, Q.X.[Qiu-Xiang], Xiong, Z.K.[Zhen-Kai],
Vision-aware target recognition toward autonomous robot by Kinect sensors,
SP:IC(84), 2020, pp. 115810.
Elsevier DOI 2004
Target recognition, Kinect sensor, HSV BibRef

Liu, G.H.[Guo-Hua], Duan, J.C.[Jian-Chun],
RGB-D image segmentation using superpixel and multi-feature fusion graph theory,
SIViP(14), No. 6, September 2020, pp. 1171-1179.
WWW Link. 2008
BibRef

Zhang, Z.Y.[Zhen-Yu], Cui, Z.[Zhen], Xu, C.Y.[Chun-Yan], Jie, Z.Q.[Ze-Qun], Li, X.[Xiang], Yang, J.[Jian],
Joint Task-Recursive Learning for RGB-D Scene Understanding,
PAMI(42), No. 10, October 2020, pp. 2608-2623.
IEEE DOI 2009
Task analysis, Estimation, Semantics, Image segmentation, Learning systems, Fuses, Cameras, Depth estimation, RGB-D scene understanding BibRef

Xiong, Z., Yuan, Y., Wang, Q.,
ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition,
IP(30), 2021, pp. 2722-2733.
IEEE DOI 2102
Feature extraction, Image recognition, Object detection, Training, Correlation, Layout, Convolution, RGB-D recognition, multi-modal feature learning BibRef

Zhao, X.L.[Xiao-Li], Chen, Z.[Zheng], Hwang, J.N.[Jenq-Neng], Shang, X.[Xiwu],
AFLNet: Adversarial focal loss network for RGB-D salient object detection,
SP:IC(94), 2021, pp. 116224.
Elsevier DOI 2104
RGB-D saliency object detection, Class imbalance, Adversarial focal loss, Inception fusion model BibRef

Du, D.P.[Da-Peng], Wang, L.M.[Li-Min], Li, Z.Y.[Zhao-Yang], Wu, G.S.[Gang-Shan],
Cross-Modal Pyramid Translation for RGB-D Scene Recognition,
IJCV(129), No. 8, August 2021, pp. 2309-2327.
Springer DOI 2108
BibRef

Du, D.P.[Da-Peng], Wang, L.M.[Li-Min], Wang, H.L.[Hui-Ling], Zhao, K.[Kai], Wu, G.S.[Gang-Shan],
Translate-to-Recognize Networks for RGB-D Scene Recognition,
CVPR19(11828-11837).
IEEE DOI 2002
BibRef

Yu, T.[Tao], Zheng, Z.R.[Ze-Rong], Guo, K.W.[Kai-Wen], Liu, P.P.[Peng-Peng], Dai, Q.H.[Qiong-Hai], Liu, Y.B.[Ye-Bin],
Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors,
CVPR21(5742-5752)
IEEE DOI 2111
Hair, Geometry, Surface reconstruction, Real-time systems, Sensor systems, Sensors BibRef

Yang, X.[Xin], Yuan, Z.[Zikang], Zhu, D.[Dongfu], Chi, C.[Cheng], Li, K.[Kun], Liao, C.Y.[Chun-Yuan],
Robust and Efficient RGB-D SLAM in Dynamic Environments,
MultMed(23), 2021, pp. 4208-4219.
IEEE DOI 2112
Dynamics, Simultaneous localization and mapping, Cameras, Pose estimation, Robustness, simultaneous localization and mapping BibRef

Yue, Y.C.[Yu-Chun], Zhou, W.J.[Wu-Jie], Lei, J.S.[Jing-Sheng], Yu, L.[Lu],
RTLNet: Recursive Triple-Path Learning Network for Scene Parsing of RGB-D Images,
SPLetters(29), 2022, pp. 429-433.
IEEE DOI 2202
Image segmentation, Semantics, Decoding, Training, Streaming media, Sensors, Feature extraction, Scene parsing, cross-modality fusion, deep learning BibRef

Caglayan, A.[Ali], Imamoglu, N.[Nevrez], Can, A.B.[Ahmet Burak], Nakamura, R.[Ryosuke],
When CNNs meet random RNNs: Towards multi-level analysis for RGB-D object and scene recognition,
CVIU(217), 2022, pp. 103373.
Elsevier DOI 2203
Convolutional Neural Networks, Randomized neural networks, Transfer learning, RGB-D object recognition, RGB-D scene recognition BibRef

Joo, K.[Kyungdon], Kim, P.[Pyojin], Hebert, M.[Martial], Kweon, I.S.[In So], Kim, H.J.[Hyoun Jin],
Linear RGB-D SLAM for Structured Environments,
PAMI(44), No. 11, November 2022, pp. 8403-8419.
IEEE DOI 2210
Simultaneous localization and mapping, Optimization, Estimation, Visualization, Kalman filters, Linear SLAM, manhattan world, scene understanding BibRef

Zhang, Y.[Ying], Yin, M.[Maoliang], Wang, H.[Heyong], Hua, C.C.[Chang-Chun],
Cross-Level Multi-Modal Features Learning With Transformer for RGB-D Object Recognition,
CirSysVideo(33), No. 12, December 2023, pp. 7121-7130.
IEEE DOI 2312
BibRef

Yang, J.[Jinyu], Gao, M.Q.[Ming-Qi], Zheng, F.[Feng], Zhen, X.T.[Xian-Tong], Ji, R.R.[Rong-Rong], Shao, L.[Ling], Leonardis, A.[Aleš],
Weakly-Supervised RGBD Video Object Segmentation,
IP(33), 2024, pp. 2158-2170.
IEEE DOI Code:
WWW Link. 2403
Annotations, Object segmentation, Training, Target tracking, Task analysis, Object tracking, Benchmark testing, RGBD data, visual tracking BibRef

Liu, X.R.[Xin-Ran], Qi, L.[Lin], Song, Y.X.[Yu-Xuan], Wen, Q.[Qi],
Depth awakens: A depth-perceptual attention fusion network for RGB-D camouflaged object detection,
IVC(143), 2024, pp. 104924.
Elsevier DOI Code:
WWW Link. 2403
Camouflaged object detection, RGB-D, Convolutional neural networks, Feature fusion BibRef

Zhang, Y.M.[Yu-Ming], Zhou, W.[Wujie], Ran, X.X.[Xiao-Xiao], Fang, M.[Meixin],
Lightweight Dual Stream Network With Knowledge Distillation for RGB-D Scene Parsing,
SPLetters(31), 2024, pp. 855-859.
IEEE DOI 2404
Streaming media, Semantic segmentation, Feature extraction, Data mining, Convolution, Sun, RGB-D scene parsing, knowledge distillation BibRef

Zhou, W.[Wujie], Xu, G.[Gao], Fang, M.X.[Mei-Xin], Mao, S.S.[Shan-Shan], Yang, R.W.[Rong-Wang], Yu, L.[Lu],
PGGNet: Pyramid gradual-guidance network for RGB-D indoor scene semantic segmentation,
SP:IC(128), 2024, pp. 117164.
Elsevier DOI 2409
Pyramid structure, RGB-D, Semantic segmentation, Modality enhancement fusion, Feature refinement BibRef

Li, C.[Chao], Zhou, W.[Wujie], Zhou, X.[Xi], Yan, W.Q.[Wei-Qing],
Semantic Progressive Guidance Network for RGB-D Mirror Segmentation,
SPLetters(31), 2024, pp. 2780-2784.
IEEE DOI 2410
Feature extraction, Mirrors, Wavelet transforms, Convolution, Correlation, Semantic segmentation, Convolutional codes, self-knowledge distillation BibRef

Li, Y.Q.[Yun-Qiang], Huang, S.[Shuowen], Chen, Y.[Ying], Ding, Y.[Yong], Zhao, P.C.[Peng-Cheng], Hu, Q.W.[Qing-Wu], Zhang, X.[Xujie],
RGBTSDF: An Efficient and Simple Method for Color Truncated Signed Distance Field (TSDF) Volume Fusion Based on RGB-D Images,
RS(16), No. 17, 2024, pp. 3188.
DOI Link 2409
BibRef

Zhou, W.[Wujie], Jian, B.[Bitao], Fang, M.[Meixin], Dong, X.[Xiena], Liu, Y.Y.[Yuan-Yuan], Jiang, Q.P.[Qiu-Ping],
DGPINet-KD: Deep Guided and Progressive Integration Network With Knowledge Distillation for RGB-D Indoor Scene Analysis,
CirSysVideo(34), No. 9, September 2024, pp. 7844-7855.
IEEE DOI Code:
WWW Link. 2410
Semantics, Feature extraction, Computational modeling, Circuits and systems, Convolution, Semantic segmentation, depth guidance BibRef

Zhou, W.[Wujie], Cai, Y.Q.[Yu-Qi], Qiang, F.F.[Fang-Fang],
Morphology-Guided Network via Knowledge Distillation for RGB-D Mirror Segmentation,
ITS(25), No. 11, November 2024, pp. 17382-17391.
IEEE DOI 2411
Feature extraction, Mirrors, Task analysis, Knowledge engineering, Semantics, Computational modeling, morphology-guided network BibRef

Zhou, W.[Wujie], Cai, Y.Q.[Yu-Qi], Zhang, L.[Liting], Yan, W.Q.[Wei-Qing], Yu, L.[Lu],
UTLNet: Uncertainty-Aware Transformer Localization Network for RGB-Depth Mirror Segmentation,
MultMed(26), 2024, pp. 4564-4574.
IEEE DOI 2403
Mirrors, Image segmentation, Feature extraction, Transformers, Uncertainty, Semantic segmentation, Semantics, transformer BibRef

Li, N.N.[Ning-Ning], Wang, X.M.[Xiao-Min], Zheng, Z.[Zhou], Sun, Z.D.[Zhen-Dong],
Self-supervised fusion network for RGB-D interest point detection and description,
PR(158), 2025, pp. 111040.
Elsevier DOI 2411
Interest point extraction in challenging indoor environment, RGB-D cross-modal fusion network (RDFNet), Self-supervised, Two-stage cross-modal reweighted feature fusion BibRef

Ghanekar, B.[Bhargav], Khan, S.S.[Salman Siddique], Sharma, P.[Pranav], Singh, S.[Shreyas], Boominathan, V.[Vivek], Mitra, K.[Kaushik], Veeraraghavan, A.[Ashok],
Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging,
CVPR24(25348-25357)
IEEE DOI 2410
Photography, Image sensors, Imaging, Apertures, Cameras, Sensors, Computational Photography, RGB-D Imaging, Coded Aperture Imaging, Dual-pixel camera BibRef

Koneputugodage, C.H.[Chamin Hewa], Ben-Shabat, Y.Z.[Yi-Zhak], Campbell, D.[Dylan], Gould, S.[Stephen],
Small Steps and Level Sets: Fitting Neural Surface Models with Point Guidance,
CVPR24(21456-21465)
IEEE DOI 2410
Measurement, Geometry, Point cloud compression, Surface reconstruction, Shape, Level set, Surface fitting, point cloud reconstruction BibRef

Xia, H.C.[Hong-Chi], Fu, Y.[Yang], Liu, S.[Sifei], Wang, X.L.[Xiao-Long],
RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos,
CVPR24(22378-22389)
IEEE DOI Code:
WWW Link. 2410
Point cloud compression, Training, Surface reconstruction, Annotations, Pose estimation BibRef

Wang, H.Y.[Heng-Yi], Wang, J.W.[Jing-Wen], Agapito, L.[Lourdes],
MorpheuS: Neural Dynamic 360° Surface Reconstruction from Monocular RGB-D Video,
CVPR24(20965-20976)
IEEE DOI Code:
WWW Link. 2410
Geometry, Surface reconstruction, Accuracy, Dynamics, Reconstruction algorithms, Rendering (computer graphics), neural field BibRef

Wu, Y.[Yushuang], Shi, L.[Luyue], Cai, J.H.[Jun-Hao], Yuan, W.H.[Wei-Hao], Qiu, L.[Lingteng], Dong, Z.L.[Zi-Long], Bo, L.[Liefeng], Cui, S.G.[Shu-Guang], Han, X.G.[Xiao-Guang],
IPoD: Implicit Field Learning with Point Diffusion for Generalizable 3D Object Reconstruction from Single RGB-D Images,
CVPR24(20432-20442)
IEEE DOI Code:
WWW Link. 2410
Portable media players, Point cloud compression, Shape, Noise reduction, Cooperative systems, Transformers BibRef

Wang, J.W.[Jing-Wen], Bleja, T.[Tymoteusz], Agapito, L.[Lourdes],
GO-Surf: Neural Feature Grid Optimization for Fast, High-Fidelity RGB-D Surface Reconstruction,
3DV22(433-442)
IEEE DOI Code:
WWW Link. 2408
Training, Surface reconstruction, Optimization methods, Benchmark testing, Rendering (computer graphics), Filling BibRef

Mohamed, M.[Mirgahney], Agapito, L.[Lourdes],
DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction With an Optimizable Feature Grid,
3DV24(820-830)
IEEE DOI Code:
WWW Link. 2408
Geometry, Surface reconstruction, Solid modeling, Deformation, Network topology, Dynamics, Monocular RGB-D Sequences from static viewpoint BibRef

Ainetter, S.[Stefan], Stekovic, S.[Sinisa], Fraundorfer, F.[Friedrich], Lepetit, V.[Vincent],
HOC-Search: Efficient CAD Model and Pose Retrieval From RGB-D Scans,
3DV24(944-953)
IEEE DOI Code:
WWW Link. 2408
Training, Point cloud compression, Solid modeling, Monte Carlo methods, Object detection, Search problems BibRef

Sun, F.Y.[Feng-Yuan], Karaoglu, S.[Sezer], Gevers, T.[Theo],
Temporally Consistent Semantic Segmentation using Spatially Aware Multi-view Semantic Fusion for Indoor RGB-D videos,
CVMeta23(4250-4259)
IEEE DOI 2401
BibRef

Ainetter, S.[Stefan], Stekovic, S.[Sinisa], Fraundorfer, F.[Friedrich], Lepetit, V.[Vincent],
Automatically Annotating Indoor Images with CAD Models via RGB-D Scans,
WACV23(3155-3163)
IEEE DOI 2302
Geometry, Solid modeling, Visualization, Annotations, Computational modeling, Algorithms: 3D computer vision BibRef

Irshad, M.Z.[Muhammad Zubair], Zakharov, S.[Sergey], Ambrus, R.[Rares], Kollar, T.[Thomas], Kira, Z.[Zsolt], Gaidon, A.[Adrien],
ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization,
ECCV22(II:275-292).
Springer DOI 2211
BibRef

Liu, Y.Z.[Yun-Ze], Chen, J.Y.[Jun-Yu], Zhang, Z.[Zekai], Huang, J.W.[Jing-Wei], Yi, L.[Li],
LeaF: Learning Frames for 4D Point Cloud Sequence Understanding,
ICCV23(604-613)
IEEE DOI 2401
BibRef

Wen, H.[Hao], Liu, Y.Z.[Yun-Ze], Huang, J.W.[Jing-Wei], Duan, B.[Bo], Yi, L.[Li],
Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding,
ECCV22(XXIX:19-35).
Springer DOI 2211
BibRef

Xiao, Z.B.[Zhi-Bin], Xie, P.W.[Peng-Wei], Wang, G.J.[Gui-Jin],
Multi-scale Cross-Modal Transformer Network for RGB-D Object Detection,
MMMod22(I:352-363).
Springer DOI 2203
BibRef

Hou, J.[Ji], Xie, S.N.[Sai-Ning], Graham, B.[Benjamin], Dai, A.[Angela], Nießner, M.[Matthias],
Pri3D: Can 3D Priors Help 2D Representation Learning?,
ICCV21(5673-5682)
IEEE DOI 2203
Representation learning, Measurement, Image segmentation, Shape, Semantics, Stereo, 3D from multiview and other sensors, 3D from a single image and shape-from-x BibRef

Clarke, J.[Joshua], Mills, S.[Steven],
Sensor Evaluation for Voxel-Based RGB-D SLAM,
IVCNZ21(1-6)
IEEE DOI 2201
BibRef

Guo, L.[Lin], Fan, G.L.[Guo-Liang],
Locop: Local Collaborative Object Presence for Semantic Labeling Via Score Map Re-Inference,
ICIP21(2219-2223)
IEEE DOI 2201
Knowledge engineering, Image segmentation, Fuses, Semantics, Collaboration, Detectors, semantic labeling of RGB-D, scene understanding BibRef

Ferreri, A.[Andrea], Bucci, S.[Silvia], Tommasi, T.[Tatiana],
Multi-Modal RGB-D Scene Recognition Across Domains,
DeepMTL21(2199-2208)
IEEE DOI 2112
Target recognition, Robot vision systems, Benchmark testing, Cameras BibRef

Fan, H.[Hehe], Zhu, L.C.[Lin-Chao], Yang, Y.[Yi], Kankanhalli, M.[Mohan],
PointListNet: Deep Learning on 3D Point Lists,
CVPR23(17692-17701)
IEEE DOI 2309
BibRef

Fan, H.[Hehe], Yang, Y.[Yi], Kankanhalli, M.[Mohan],
Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos,
CVPR21(14199-14208)
IEEE DOI 2111
Solid modeling, Convolution, Tracking, Computational modeling, Semantics, Transformers BibRef

Bokhovkin, A.[Alexey], Ishimtsev, V.[Vladislav], Bogomolov, E.[Emil], Zorin, D.[Denis], Artemov, A.[Alexey], Burnaev, E.[Evgeny], Dai, A.[Angela],
Towards Part-Based Understanding of RGB-D Scans,
CVPR21(7480-7490)
IEEE DOI 2111
Geometry, Semantics, Buildings, Cognition, Autonomous agents BibRef

Chen, R., Zhang, F.L., Rhee, T.,
Edge-Aware Convolution for RGB-D Image Segmentation,
IVCNZ20(1-6)
IEEE DOI 2012
Image segmentation, Convolution, Image edge detection, Semantics, Feature extraction, Kernel, Edge-Aware BibRef

Avetisyan, A.[Armen], Khanova, T.[Tatiana], Choy, C.[Christopher], Dash, D.[Denver], Dai, A.[Angela], Nießner, M.[Matthias],
SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans,
ECCV20(XXII:596-612).
Springer DOI 2011
BibRef

Back, S., Kim, J., Kang, R., Choi, S., Lee, K.,
Segmenting Unseen Industrial Components In A Heavy Clutter Using RGB-D Fusion And Synthetic Data,
ICIP20(828-832)
IEEE DOI 2011
Data models, Clutter, Solid modeling, Shape, Training, Computational modeling, Machine learning, Synthetic Data BibRef

Xing, Y.J.[Ya-Jie], Wang, J.B.[Jing-Bo], Zeng, G.[Gang],
Malleable 2.5d Convolution: Learning Receptive Fields Along the Depth-axis for RGB-D Scene Parsing,
ECCV20(XIX:555-571).
Springer DOI 2011
BibRef

Fu, Y.P.[Yan-Ping], Yan, Q.A.[Qing-An], Liao, J.[Jie], Xiao, C.X.[Chun-Xia],
Joint Texture and Geometry Optimization for RGB-D Reconstruction,
CVPR20(5949-5958)
IEEE DOI 2008
Cameras, Geometry, Image reconstruction, Optimization, Solid modeling, Color BibRef

Hou, J., Dai, A., Nießner, M.,
RevealNet: Seeing Behind Objects in RGB-D Scans,
CVPR20(2095-2104)
IEEE DOI 2008
Geometry, Semantics, Task analysis, Feature extraction, Object detection BibRef

Halber, M., Shi, Y., Xu, K., Funkhouser, T.,
Rescan: Inductive Instance Segmentation for Indoor RGBD Scans,
ICCV19(2541-2550)
IEEE DOI 2004
image colour analysis, image scanners, image segmentation, object tracking, inductive instance segmentation, Cameras BibRef

Yi, L.[Li], Zhao, W.[Wang], Wang, H.[He], Sung, M.[Minhyuk], Guibas, L.J.[Leonidas J.],
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud,
CVPR19(3942-3951).
IEEE DOI 2002
BibRef

Mishima, M.[Masashi], Uchiyama, H.[Hideaki], Thomas, D.[Diego], Taniguchi, R.I.[Rin-Ichiro], Roberto, R.[Rafael], Lima, J.P.[João Paulo], Teichrieb, V.[Veronica],
RGB-D SLAM Based Incremental Cuboid Modeling,
3D-Wild18(I:414-429).
Springer DOI 1905
BibRef

Li, W., Xiao, X., Hahn, J.,
3D Reconstruction and Texture Optimization Using a Sparse Set of RGB-D Cameras,
WACV19(1413-1422)
IEEE DOI 1904
cameras, image colour analysis, image reconstruction, image registration, image sensors, image texture, Sensors BibRef

Park, J.J., Newcombe, R.A., Seitz, S.M.,
Surface Light Field Fusion,
3DV18(12-21)
IEEE DOI 1812
image colour analysis, image reconstruction, image sensors, highly reflective objects, commodity RGBD sensor, RGBD BibRef

Kaiser, A.[Adrien], Zepeda, J.A.Y.[Jose Alonso Ybanez], Boubekeur, T.[Tamy],
Proxy Clouds for Live RGB-D Stream Processing and Consolidation,
ECCV18(VI: 255-271).
Springer DOI 1810
BibRef

Kim, P.[Pyojin], Coltin, B.[Brian], Kim, H.J.[H. Jin],
Linear RGB-D SLAM for Planar Environments,
ECCV18(II: 350-366).
Springer DOI 1810
BibRef

Shi, Y.F.[Yi-Fei], Xu, K.[Kai], Nießner, M.[Matthias], Rusinkiewicz, S.[Szymon], Funkhouser, T.[Thomas],
PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction,
ECCV18(VIII: 767-784).
Springer DOI 1810
BibRef

Stefanczyk, M.[Maciej],
Improving RGB Descriptors Using Depth Cues,
ICCVG18(251-262).
Springer DOI 1810
BibRef

Wang, W.[Weiyue], Neumann, U.[Ulrich],
Depth-Aware CNN for RGB-D Segmentation,
ECCV18(XI: 144-161).
Springer DOI 1810
BibRef

Yi, R., Liu, Y., Lai, Y.,
Evaluation on the Compactness of Supervoxels,
ICIP18(2212-2216)
IEEE DOI 1809
Measurement, Videos, Shape, Correlation, Spatiotemporal phenomena, Complexity theory, Color, Supervoxel, compactness, metric evaluation BibRef

Aakerberg, A., Nasrollahi, K., Heder, T.,
Improving a deep learning based RGB-D object recognition model by ensemble learning,
IPTA17(1-6)
IEEE DOI 1804
convolution, feedforward neural nets, image colour analysis, learning (artificial intelligence), object recognition, RGB-D BibRef

Chen, C.F., Bolas, M., Rosenberg, E.S.,
View-dependent virtual reality content from RGB-D images,
ICIP17(2931-2935)
IEEE DOI 1803
Cameras, Color, Computational modeling, Image color analysis, Rendering (computer graphics), Solid modeling, Virtual Reality BibRef

Zhang, M., Kadam, P., Liu, S., Kuo, C.C.J.,
Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation,
VCIP20(144-147)
IEEE DOI 2102
Image segmentation, Correlation, Shape, Visual communication, Feature extraction, successive subspace learning BibRef

Fu, X., Chen, C., Li, J., Wang, C., Kuo, C.C.J.,
Image segmentation using contour, surface, and depth cues,
ICIP17(81-85)
IEEE DOI 1803
Copper, Estimation, Image edge detection, Image segmentation, Reliability, Spectral Graph BibRef

Trabelsi, R.[Rim], Jabri, I.[Issam], Melgani, F.[Farid], Smach, F.[Fethi], Conci, N.[Nicola], Bouallegue, A.[Ammar],
Complex-Valued Representation for RGB-D Object Recognition,
PSIVT17(17-27).
Springer DOI 1802
BibRef

Ahmadi, S.S., Khotanlou, H.,
Enhance support relation extraction accuracy using improvement of segmentation in RGB-D images,
IPRIA17(166-169)
IEEE DOI 1712
feature extraction, image colour analysis, image enhancement, image segmentation, Support relation extraction BibRef

Hodan, T.[Tomáš], Haluza, P.[Pavel], Obdržálek, Š.[Štepán], Matas, J.G.[Jirí G.], Lourakis, M.[Manolis], Zabulis, X.[Xenophon],
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-Less Objects,
WACV17(880-888)
IEEE DOI 1609
Dataset, RBG-D.
WWW Link. (Slow response) Image color analysis, Image sensors, Pose estimation, Sensors, Solid modeling, Training BibRef

Perez-Yus, A.[Alejandro], Bermudez-Cameo, J.[Jesus], Guerrero, J.J.[Jose J.], Lopez-Nicolas, G.[Gonzalo],
Depth and Motion Cues with Phosphene Patterns for Prosthetic Vision,
ACVR17(1516-1525)
IEEE DOI 1802
Cameras, Image resolution, Navigation, Prosthetics, Visualization BibRef

Bermudez-Cameo, J.[Jesus], Badias-Herbera, A.[Alberto], Guerrero-Viu, M.[Manuel], Lopez-Nicolas, G.[Gonzalo], Guerrero, J.J.[Jose J.],
RGB-D Computer Vision Techniques for Simulated Prosthetic Vision,
IbPRIA17(427-436).
Springer DOI 1706
BibRef

Chen, B., Yang, J.H.[Jian-Hao], Ding, M.[Mengru], Liu, T.L.[Tian-Liang], Zhang, X.P.[Xin-Peng],
Quaternion-type moments combining both color and depth information for RGB-D object recognition,
ICPR16(704-708)
IEEE DOI 1705
Color, Face, Image color analysis, Neurons, Object recognition, Quaternions, Redundancy, RGB-D object recognition, color image, depth information, quaternion, moment BibRef

Yang, C.L.[Cheng-Liang], Sethi, M.[Manu], Rangarajan, A.[Anand], Ranka, S.[Sanjay],
Supervoxel-Based Segmentation of 3D Volumetric Images,
ACCV16(I: 37-53).
Springer DOI 1704
BibRef

Drozdov, G.[Gilad], Shapiro, Y.[Yevgengy], Gilboa, G.[Guy],
Robust Recovery of Heavily Degraded Depth Measurements,
3DV16(56-65)
IEEE DOI 1701
image colour analysis BibRef

Seychell, D.[Dylan], Debono, C.J.[Carl James],
Efficient object selection using depth and texture information,
VCIP16(1-4)
IEEE DOI 1701
Image color analysis BibRef

Lombardi, S., Nishino, K.,
Radiometric Scene Decomposition: Scene Reflectance, Illumination, and Geometry from RGB-D Images,
3DV16(305-313)
IEEE DOI 1701
computer vision BibRef

Li, S., Handa, A., Zhang, Y., Calway, A.,
HDRFusion: HDR SLAM Using a Low-Cost Auto-Exposure RGB-D Sensor,
3DV16(314-322)
IEEE DOI 1701
SLAM (robots) BibRef

Georgakis, G., Reza, M.A., Mousavian, A., Le, P.H., KošeckŽá, J.,
Multiview RGB-D Dataset for Object Instance Detection,
3DV16(426-434)
IEEE DOI 1701
Clutter BibRef

Liu, C.[Chen], Kohli, P.[Pushmeet], Furukawa, Y.[Yasutaka],
Layered Scene Decomposition via the Occlusion-CRF,
CVPR16(165-173)
IEEE DOI 1612
BibRef

Sharma, A.[Abhishek], Grau, O.[Oliver], Fritz, M.[Mario],
VConv-DAE: Deep Volumetric Shape Learning Without Object Labels,
DeepLearn16(III: 236-250).
Springer DOI 1611
BibRef

Tzionas, D.[Dimitrios], Gall, J.[Juergen],
Reconstructing Articulated Rigged Models from RGB-D Videos,
6DPose16(III: 620-633).
Springer DOI 1611
BibRef

Liu, X.D.[Xin-Da], Wang, X.M.[Xue-Ming], Jiang, S.Q.[Shu-Qiang],
RGB-D scene classification via heterogeneous model fusion,
ICIP16(499-503)
IEEE DOI 1610
Boolean functions BibRef

Feng, J.[Jie], Wang, Y.[Yan], Chang, S.F.[Shih-Fu],
3D shape retrieval using a single depth image from low-cost sensors,
WACV16(1-9)
IEEE DOI 1606
Computational modeling. RGB-D sensors. BibRef

Liu, S., Li, W., Ogunbona, P., Chow, Y.W.,
Creating Simplified 3D Models with High Quality Textures,
DICTA15(1-8)
IEEE DOI 1603
RGB-D data. BibRef

Zaki, H.F.M., Shafait, F., Mian, A.,
Modeling 2D Appearance Evolution for 3D Object Categorization,
DICTA16(1-8)
IEEE DOI 1701
BibRef
Earlier:
Localized Deep Extreme Learning Machines for Efficient RGB-D Object Recognition,
DICTA15(1-8)
IEEE DOI 1603
image classification BibRef

Wetherall, J., Taylor, M., Hurley-Smith, D.,
Investigation into the effects of transmission-channel fidelity loss in RGBD sensor data for SLAM,
WSSIP15(81-84)
IEEE DOI 1603
SLAM (robots) BibRef

Pham, T.T.[Trung T.], Reid, I.D.[Ian D.], Latif, Y.[Yasir], Gould, S.[Stephen],
Hierarchical Higher-Order Regression Forest Fields: An Application to 3D Indoor Scene Labelling,
ICCV15(2246-2254)
IEEE DOI 1602
RGB-D. Computational modeling BibRef

Cheng, Y., Cai, R., Zhang, C., Li, Z., Zhao, X., Huang, K., Rui, Y.,
Query Adaptive Similarity Measure for RGB-D Object Recognition,
ICCV15(145-153)
IEEE DOI 1602
Art BibRef

Hachama, M., Ghanem, B., Wonka, P.,
Intrinsic Scene Decomposition from RGB-D Images,
ICCV15(810-818)
IEEE DOI 1602
Coherence BibRef

Soni, N.[Nishit], Namboodiri, A.M.[Anoop M.], Jawahar, C.V., Ramalingam, S.[Srikumar],
Semantic Classification of Boundaries of an RGBD Image,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Liu, J.[Jing], Ren, T.[Tongwei], Bei, J.[Jia],
Elastic Edge Boxes for Object Proposal on RGB-D Images,
MMMod16(I: 199-211).
Springer DOI 1601
BibRef

Petrelli, A.[Alioscia], di Stefano, L.[Luigi],
Learning to Weight Color and Depth for RGB-D Visual Search,
CIAP17(I:648-659).
Springer DOI 1711
BibRef

Petrelli, A.[Alioscia], Pau, D.[Danilo], di Stefano, L.[Luigi],
Analysis of Compact Features for RGB-D Visual Search,
CIAP15(II:14-24).
Springer DOI 1511
BibRef

Geiger, A.[Andreas], Wang, C.H.[Chao-Hui],
Joint 3D Object and Layout Inference from a Single RGB-D Image,
GCPR15(183-195).
Springer DOI 1511
Award, GCPR. BibRef

Nakaguro, Y.[Yoichi], Qureshi, W.S.[Waqar S.], Dailey, M.N.[Matthew N.], Ekpanyapong, M.[Mongkol], Bunnun, P.[Pished], Tungpimolrut, K.[Kanokvate],
Volumetric 3D Reconstruction and Parametric Shape Modeling from RGB-D Sequences,
CIAP15(I:500-516).
Springer DOI 1511
BibRef

Zhu, C.[Chen], Bilgeri, S.[Simon], Günther, C.[Christoph],
Spatial Uncertainty Model of a Three-View RGB-D Camera System,
ISVC14(II: 117-128).
Springer DOI 1501
Combine 2 view stereo with RBG-D for more complete modeling. BibRef

Fu, H.Z.[Hua-Zhu], Xu, D.[Dong], Lin, S.[Stephen], Liu, J.[Jiang],
Object-based RGBD image co-segmentation with mutex constraint,
CVPR15(4428-4436)
IEEE DOI 1510
BibRef

Cinque, L.[Luigi], Danani, A.[Alessandro], Dondi, P.[Piercarlo], Lombardi, L.[Luca],
Real-Time Foreground Segmentation with Kinect Sensor,
CIAP15(II:56-65).
Springer DOI 1511
BibRef

Wan, S.H.[Shao-Hua], Aggarwal, J.K.,
Robust object recognition in RGB-D egocentric videos based on Sparse Affine Hull Kernel,
PBVS15(97-104)
IEEE DOI 1510
Cameras BibRef

Diebold, J.[Julia], Demmel, N.[Nikolaus], Hazirbas, C.[Caner], Moeller, M.[Michael], Cremers, D.[Daniel],
Interactive Multi-label Segmentation of RGB-D Images,
SSVM15(294-306).
Springer DOI 1506
BibRef

Nghiem, A.T.[Anh-Tuan], Bremond, F.[Francois],
Background subtraction in people detection framework for RGB-D cameras,
AVSS14(241-246)
IEEE DOI 1411
Cameras BibRef

Trabelsi, R.[Rim], Smach, F.[Fethi], Jabri, I.[Issam], Bouallegue, A.[Ammar],
Multimodal Background Modeling Using RGB-Depth Features,
CIARP14(884-892).
Springer DOI 1411
BibRef

Hickson, S.[Steven], Essa, I.[Irfan], Christensen, H.[Henrik],
Semantic Instance Labeling Leveraging Hierarchical Segmentation,
WACV15(1068-1075)
IEEE DOI 1503
Accuracy BibRef

Hickson, S.[Steven], Birchfield, S.[Stan], Essa, I.[Irfan], Christensen, H.[Henrik],
Efficient Hierarchical Graph-Based Segmentation of RGBD Videos,
CVPR14(344-351)
IEEE DOI 1409
4D Segmentation BibRef

Barrera, F.[Fernando], Padoy, N.[Nicolas],
Piecewise Planar Decomposition of 3D Point Clouds Obtained from Multiple Static RGB-D Cameras,
3DV14(194-201)
IEEE DOI 1503
Cameras BibRef

Lim, H.[Hyon], Lim, J.W.[Jong-Woo], Kim, H.J.[H. Jin],
Online 3D Reconstruction and 6-DoF Pose Estimation for RGB-D Sensors,
CVVT14(238-254).
Springer DOI 1504
BibRef

Zhang, Q.S.[Quan-Shi], Song, X.[Xuan], Shao, X.W.[Xiao-Wei], Zhao, H.J.[Hui-Jing], Shibasaki, R.[Ryosuke],
When 3D Reconstruction Meets Ubiquitous RGB-D Images,
CVPR14(700-707)
IEEE DOI 1409
3D reconstruction. Using category models. Rotation, 3D and texture. BibRef

Kerl, C.[Christian], Souiai, M.[Mohamed], Sturm, J.[Jurgen], Cremers, D.[Daniel],
Towards Illumination-Invariant 3D Reconstruction Using ToF RGB-D Cameras,
3DV14(39-46)
IEEE DOI 1503
Cameras BibRef

Perera, S.[Samunda], Barnes, N.M.[Nick M.],
1-Point Rigid Motion Estimation and Segmentation with a RGB-D Camera,
DICTA13(1-8)
IEEE DOI 1402
BibRef
Earlier:
Maximal Cliques Based Rigid Body Motion Segmentation with a RGB-D Camera,
ACCV12(II:120-133).
Springer DOI 1304
BibRef
Earlier:
A Simple and Practical Solution to the Rigid Body Motion Segmentation Problem Using a RGB-D Camera,
DICTA11(494-500).
IEEE DOI 1205
cameras BibRef

Steinbrucker, F.[Frank], Kerl, C.[Christian], Cremers, D.[Daniel],
Large-Scale Multi-resolution Surface Reconstruction from RGB-D Sequences,
ICCV13(3264-3271)
IEEE DOI 1403
BibRef

Weikersdorfer, D.[David], Schick, A.[Alexander], Cremers, D.[Daniel],
Depth-adaptive supervoxels for RGB-D video segmentation,
ICIP13(2708-2712)
IEEE DOI 1402
RGB-D; Superpixels; Supervoxels; Video Analysis; Video Segmentation BibRef

Guan, L.[Li], Yu, T.[Ting], Tu, P.[Peter], Lim, S.N.[Ser-Nam],
Simultaneous image segmentation and 3D plane fitting for RGB-D sensors: An iterative framework,
PCP12(49-56).
IEEE DOI 1207
BibRef

Srinivasan, N.[Natesh], Dellaert, F.[Frank],
A Rao-Blackwellized MCMC algorithm for recovering piecewise planar 3D models from multiple view RGBD images,
ICIP14(5392-5392)
IEEE DOI 1502
Bayes methods BibRef

Dellaert, F.[Frank],
Factor Graphs for Fast and Scalable 3D Reconstruction and Mapping,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Erdogan, C.[Can], Paluri, M.[Manohar], Dellaert, F.[Frank],
Planar Segmentation of RGBD Images Using Fast Linear Fitting and Markov Chain Monte Carlo,
CRV12(32-39).
IEEE DOI 1207
BibRef

Weikersdorfer, D.[David], Gossow, D.[David], Beetz, M.[Michael],
Depth-adaptive superpixels,
ICPR12(2087-2090).
WWW Link. 1302
BibRef

Kim, B.S.[Byung-Soo], Kohli, P.[Pushmeet], Savarese, S.[Silvio],
3D Scene Understanding by Voxel-CRF,
ICCV13(1425-1432)
IEEE DOI 1403
3D reconstruction; RGB-D; Scene understanding BibRef

Sallem, N.K.[Nizar K.], Devy, M.[Michel],
Extended GrabCut for 3D and RGB-D Point Clouds,
ACIVS13(354-365).
Springer DOI 1311
BibRef

Specht, A.R., Devy, M.,
Surface segmentation using a modified ball-pivoting algorithm,
ICIP04(III: 1931-1934).
IEEE DOI 0505
BibRef

Sappa, A.D., Devy, M.,
Fast range image segmentation by an edge detection strategy,
3DIM01(292-299).
IEEE DOI 0106
BibRef

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
RGB-D Salient Object Segmentation and Detection .