19.4.3.13 Point Cloud Up-Sampling

Yang, Q.X.[Qing-Xiong], Ahuja, N., Yang, R.G.[Rui-Gang], Tan, K.H.[Kar-Han], Davis, J., Culbertson, B., Apostolopoulos, J., Wang, G.[Gang],
Fusion of Median and Bilateral Filtering for Range Image Upsampling,
IP(22), No. 12, 2013, pp. 4841-4852.
IEEE DOI 1312
image colour analysis BibRef

Park, J.[Jaesik], Kim, H.W.[Hyeong-Woo], Tai, Y.W.[Yu-Wing], Brown, M.S.[Michael S.], Kweon, I.S.[In So],
High-Quality Depth Map Upsampling and Completion for RGB-D Cameras,
IP(23), No. 12, December 2014, pp. 5559-5572.
IEEE DOI 1412
BibRef
Earlier:
High quality depth map upsampling for 3D-TOF cameras,
ICCV11(1623-1630).
IEEE DOI 1201
cameras. Upsample ToF camera using high-res image. BibRef

Choi, J.[Jinsoo], Park, J.[Jaesik], Kweon, I.S.[In So],
High-quality Frame Interpolation via Tridirectional Inference,
WACV21(596-604)
IEEE DOI 2106
Industries, Interpolation, Media, Reliability, Data mining BibRef

Lee, H.[Hyunmin], Park, J.[Jaesik],
STAD: Stable Video Depth Estimation,
ICIP21(3213-3217)
IEEE DOI 2201
Geometry, Image processing, Aggregates, Estimation, Propagation losses, Task analysis, 3D Geometry BibRef

Kang, M.K.[Min-Koo], Kim, D.Y.[Dae-Young], Yoon, K.J.[Kuk-Jin],
Adaptive Support of Spatial-Temporal Neighbors for Depth Map Sequence Up-sampling,
SPLetters(21), No. 2, February 2014, pp. 150-154.
IEEE DOI 1402
Markov processes BibRef

Kim, J.[Joohyeok], Jeon, G.G.[Gwang-Gil], Jeong, J.C.[Je-Chang],
Joint-adaptive bilateral depth map upsampling,
SP:IC(29), No. 4, 2014, pp. 506-513.
Elsevier DOI 1404
Depth upsampling BibRef

Choi, O.[Ouk], Jung, S.W.[Seung-Won],
A Consensus-Driven Approach for Structure and Texture Aware Depth Map Upsampling,
IP(23), No. 8, August 2014, pp. 3321-3335.
IEEE DOI 1408
image colour analysis BibRef

Huang, W.Q.[Wen-Qi], Gong, X.J.[Xiao-Jin], Yang, M.Y.,
Joint Object Segmentation and Depth Upsampling,
SPLetters(22), No. 2, February 2015, pp. 192-196.
IEEE DOI 1410
Markov processes BibRef

Zhu, X., Song, X., Chen, X.,
Image Guided Depth Map Upsampling using Anisotropic TV-L2,
SPLetters(22), No. 3, March 2015, pp. 318-321.
IEEE DOI 1410
Cameras BibRef

Liu, W.[Wei], Jia, S.Y.[Shao-Yong], Li, P.[Penglin], Chen, X.G.[Xiao-Gang], Yang, J.[Jie], Wu, Q.A.[Qi-Ang],
An MRF-Based Depth Upsampling: Upsample the Depth Map With Its Own Property,
SPLetters(22), No. 10, October 2015, pp. 1708-1712.
IEEE DOI 1506
Markov processes BibRef

Liu, W.[Wei], Li, P.[Penglin], Yang, J.[Jie], Shi, P.F.[Peng-Fei],
Upsampling the depth map with its own properties,
ICIP15(3530-3534)
IEEE DOI 1512
Bilateral filter;ToF;depth map upsampling;optimization BibRef

Hua, K.L.[Kai-Lung], Lo, K.H.[Kai-Han], Wang, Y.C.F.[Y.C. Frank],
Extended Guided Filtering for Depth Map Upsampling,
MultMedMag(23), No. 2, April 2016, pp. 72-83.
IEEE DOI 1605
Cameras. filtering theory BibRef

Lo, K.H.[Kai-Han], Wang, Y.C.F.[Y.C. Frank], Hua, K.L.[Kai-Lung],
Edge-Preserving Depth Map Upsampling by Joint Trilateral Filter,
Cyber(48), No. 1, January 2018, pp. 371-384.
IEEE DOI 1801
BibRef
Earlier:
Joint trilateral filtering for depth map super-resolution,
VCIP13(1-6)
IEEE DOI 1402
Color, Image color analysis, Image edge detection, Image resolution, Image sensors, Kernel, Sensors, range sensor BibRef

Al Ismaeil, K.[Kassem], Aouada, D.[Djamila], Mirbach, B.[Bruno], Ottersten, B.[Björn],
Enhancement of dynamic depth scenes by upsampling for precise super-resolution (UP-SR),
CVIU(147), No. 1, 2016, pp. 38-49.
Elsevier DOI 1605
BibRef
Earlier:
Dynamic super resolution of depth sequences with non-rigid motions,
ICIP13(660-664)
IEEE DOI 1402
Super-resolution BibRef

Al Ismaeil, K.[Kassem], Aouada, D.[Djamila], Solignac, T.[Thomas], Mirbach, B.[Bruno], Ottersten, B.[Bjorn],
Real-Time Enhancement of Dynamic Depth Videos with Non-Rigid Deformations,
PAMI(39), No. 10, October 2017, pp. 2045-2059.
IEEE DOI 1709
BibRef
Earlier:
Real-time non-rigid multi-frame depth video super-resolution,
FusionDynamic15(8-16)
IEEE DOI 1510
Cameras, Heuristic algorithms, Image resolution, Real-time systems, Videos, Depth enhancement, Kalman filtering, bilateral total variation, non-rigid deformations, registration, super-resolution BibRef

Jung, C.[Cheolkon], Yu, S.T.[Sheng-Tao], Kim, J.[Joongkyu],
Intensity-guided edge-preserving depth upsampling through weighted L0 gradient minimization,
JVCIR(42), No. 1, 2017, pp. 132-144.
Elsevier DOI 1701
BibRef
Earlier: A2, A1, A3:
Color-guided boundary-preserving depth upsampling based on L0 gradient minimization,
VCIP16(1-4)
IEEE DOI 1701
Depth upsampling. Cameras BibRef

Eichhardt, I.[Iván], Chetverikov, D.[Dmitry], Jankó, Z.[Zsolt],
Image-guided ToF depth upsampling: a survey,
MVA(28), No. 3-4, May 2017, pp. 267-282.
WWW Link. 1704
Survey, Depth Super Resolution. BibRef

Yuan, L.[Liang], Jin, X.[Xin], Li, Y.G.[Yang-Guang], Yuan, C.[Chun],
Depth map super-resolution via low-resolution depth guided joint trilateral up-sampling,
JVCIR(46), No. 1, 2017, pp. 280-291.
Elsevier DOI 1706
Joint, trilateral, upsampling BibRef

Li, Y.G.[Yang-Guang], Zhang, L.[Lei], Zhang, Y.B.[Yong-Bing], Xuan, H.M.[Hui-Ming], Dai, Q.H.[Qiong-Hai],
Depth map super-resolution via iterative joint-trilateral-upsampling,
VCIP14(386-389)
IEEE DOI 1504
image colour analysis BibRef

Liu, W., Chen, X., Yang, J., Wu, Q.,
Variable Bandwidth Weighting for Texture Copy Artifact Suppression in Guided Depth Upsampling,
CirSysVideo(27), No. 10, October 2017, pp. 2072-2085.
IEEE DOI 1710
Bandwidth, Color, Computational efficiency, DH-HEMTs, Image color analysis, Image resolution, Kernel, Blur of depth discontinuities, color image-guided depth upsampling, texture copy artifacts, variable, bandwidth, weighting BibRef

Chang, T.A.[Ting-An], Yang, J.F.[Jar-Ferr],
Precise depth map upsampling and enhancement based on edge-preserving fusion filters,
IET-CV(12), No. 5, August 2018, pp. 651-658.
DOI Link 1807
BibRef

Qiao, Y., Jiao, L., Yang, S., Hou, B.,
A Novel Segmentation Based Depth Map Up-Sampling,
MultMed(21), No. 1, January 2019, pp. 1-14.
IEEE DOI 1901
Image segmentation, Color, Image color analysis, Visualization, Merging, Interpolation, joint trilateral filtering BibRef

Belhi, A.[Abdelhak], Bouras, A.[Abdelaziz], Alfaqheri, T.[Taha], Aondoakaa, A.S.[Akuha Solomon], Sadka, A.H.[Abdul Hamid],
Investigating 3D holoscopic visual content upsampling using super-resolution for cultural heritage digitization,
SP:IC(75), 2019, pp. 188-198.
Elsevier DOI 1906
Cultural heritage, Deep learning, Super-resolution, 3D holoscopic imaging BibRef

Yang, Y.[Yoonmo], Lee, H.S.[Hean Sung], Oh, B.T.[Byung Tae],
Depth map upsampling with a confidence-based joint guided filter,
SP:IC(77), 2019, pp. 40-48.
Elsevier DOI 1906
Upsampling, Super-resolution, Depth map, Confidence map, Guided filter BibRef

Chen, J.[Jian], Zhang, Z.C.[Zi-Chao], Zhang, K.[Kai], Wang, S.[Shubo], Han, Y.[Yu],
UAV-Borne LiDAR Crop Point Cloud Enhancement Using Grasshopper Optimization and Point Cloud Up-Sampling Network,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Wang, Z.H.[Zhi-Hui], Ye, X.C.[Xin-Chen], Sun, B.L.[Bao-Li], Yang, J.Y.[Jing-Yu], Xu, R.[Rui], Li, H.J.[Hao-Jie],
Depth upsampling based on deep edge-aware learning,
PR(103), 2020, pp. 107274.
Elsevier DOI 2005
Upsampling, CNN, Edge-aware, Depth map BibRef

Yang, H.[Hang], Zhang, Z.B.[Zhong-Bo],
Depth image upsampling based on guided filter with low gradient minimization,
VC(36), No. 7, July 2020, pp. 1411-1422.
WWW Link. 2005
BibRef

Balure, C.S.[Chandra Shaker], Kini, M.R.[M. Ramesh],
Guidance-based improved depth upsampling with better initial estimate,
IJCVR(11), No. 1, 2021, pp. 109-125.
DOI Link 2012
BibRef

Li, W.[Weite], Hasegawa, K.[Kyoko], Li, L.[Liang], Tsukamoto, A.[Akihiro], Tanaka, S.[Satoshi],
Deep Learning-Based Point Upsampling for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Qian, Y.[Yue], Hou, J.H.[Jun-Hui], Kwong, S.[Sam], He, Y.[Ying],
Deep Magnification-Flexible Upsampling Over 3D Point Clouds,
IP(30), 2021, pp. 8354-8367.
IEEE DOI 2110
Geometry, Feature extraction, Training, Surface reconstruction, Image reconstruction, Deep learning, surface reconstruction BibRef

Ding, D.D.[Dan-Dan], Qiu, C.[Chi], Liu, F.[Fuchang], Pan, Z.[Zhigeng],
Point Cloud Upsampling via Perturbation Learning,
CirSysVideo(31), No. 12, December 2021, pp. 4661-4672.
IEEE DOI 2112
Feature extraction, Perturbation methods, Image reconstruction, Geometry, neural network BibRef

Zhang, P.P.[Ping-Ping], Wang, X.[Xu], Ma, L.[Lin], Wang, S.Q.[Shi-Qi], Kwong, S.[Sam], Jiang, J.M.[Jian-Min],
Progressive Point Cloud Upsampling via Differentiable Rendering,
CirSysVideo(31), No. 12, December 2021, pp. 4673-4685.
IEEE DOI 2112
Rendering (computer graphics), Task analysis, Image reconstruction, Surface reconstruction, feature expansion unit BibRef

Wang, K.Y.[Kaisi-Yuan], Sheng, L.[Lu], Gu, S.H.[Shu-Hang], Xu, D.[Dong],
Sequential Point Cloud Upsampling by Exploiting Multi-Scale Temporal Dependency,
CirSysVideo(31), No. 12, December 2021, pp. 4686-4696.
IEEE DOI 2112
Feature extraction, Shape, Task analysis, Superresolution, Estimation, Solid modeling, spatio-temporal exploration BibRef

Han, B.[Bing], Zhang, X.Y.[Xin-Yun], Ren, S.[Shuang],
PU-GACNet: Graph Attention Convolution Network for Point Cloud Upsampling,
IVC(118), 2022, pp. 104371.
Elsevier DOI 2202
Point cloud upsampling, Graph attention convolution, Feature extraction, Edge-aware nodeshuffle, Feature expansion BibRef

Wang, K.Y.[Kaisi-Yuan], Sheng, L.[Lu], Gu, S.H.[Shu-Hang], Xu, D.[Dong],
VPU: A Video-Based Point Cloud Upsampling Framework,
IP(31), 2022, pp. 4062-4075.
IEEE DOI 2206
Point cloud compression, Feature extraction, Task analysis, Graphics processing units, Image reconstruction, Cloud computing, spatial-temporal aggregation BibRef

Liu, X.H.[Xin-Hai], Liu, X.C.[Xin-Chen], Liu, Y.S.[Yu-Shen], Han, Z.Z.[Zhi-Zhong],
SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction With Self-Projection Optimization,
IP(31), 2022, pp. 4213-4226.
IEEE DOI 2207
Point cloud compression, Feature extraction, Surface reconstruction, Deep learning, Shape, Optimization, self-projection BibRef

Gu, F.[Fan], Zhang, C.[Changlun], Wang, H.[Hengyou], He, Q.[Qiang], Huo, L.[Lianzhi],
PU-WGCN: Point Cloud Upsampling Using Weighted Graph Convolutional Networks,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Liu, H.[Hao], Yuan, H.[Hui], Hou, J.H.[Jun-Hui], Hamzaoui, R.[Raouf], Gao, W.[Wei],
PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling,
IP(31), 2022, pp. 7389-7402.
IEEE DOI 2212
Point cloud compression, Geometry, Measurement, Training, Visualization, Feature extraction, Point cloud upsampling, deep learning BibRef

Akhtar, A.[Anique], Li, Z.[Zhu], van der Auwera, G.[Geert], Li, L.[Li], Chen, J.L.[Jian-Le],
PU-Dense: Sparse Tensor-Based Point Cloud Geometry Upsampling,
IP(31), 2022, pp. 4133-4148.
IEEE DOI 2206
Point cloud compression, Laser radar, Geometry, Feature extraction, Sensors, Deep learning, Point cloud, upsampling BibRef

Rozsa, Z.[Zoltan], Sziranyi, T.[Tamas],
Optical Flow and Expansion Based Deep Temporal Up-Sampling of LIDAR Point Clouds,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
BibRef

Zhao, W.B.[Wen-Bo], Liu, X.M.[Xian-Ming], Zhai, D.[Deming], Jiang, J.J.[Jun-Jun], Ji, X.Y.[Xiang-Yang],
Self-Supervised Arbitrary-Scale Implicit Point Clouds Upsampling,
PAMI(45), No. 10, October 2023, pp. 12394-12407.
IEEE DOI 2310
BibRef

Zhao, W.B.[Wen-Bo], Liu, X.M.[Xian-Ming], Zhong, Z.W.[Zhi-Wei], Jiang, J.J.[Jun-Jun], Gao, W.[Wei], Li, G.[Ge], Ji, X.Y.[Xiang-Yang],
Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation,
CVPR22(1989-1997)
IEEE DOI 2210
Point cloud compression, Visualization, Codes, Supervised learning, Self-supervised learning, Low-level vision, Self- semi- meta- unsupervised learning BibRef

Li, Z.Z.[Zhuang-Zi], Li, G.[Ge], Li, T.H.[Thomas H.], Liu, S.[Shan], Gao, W.[Wei],
Semantic Point Cloud Upsampling,
MultMed(25), 2023, pp. 3432-3442.
IEEE DOI 2309
BibRef

Zhao, T.M.[Tian-Ming], Li, L.F.[Lin-Feng], Tian, T.[Tian], Ma, J.Y.[Jia-Yi], Tian, J.W.[Jin-Wen],
APUNet: Attention-guided upsampling network for sparse and non-uniform point cloud,
PR(143), 2023, pp. 109796.
Elsevier DOI 2310
Point cloud upsampling, Distance prior, DisTransformer, Attention mechanism, Feature prediction BibRef

Han, B.[Bing], Deng, L.X.[Li-Xiang], Zheng, Y.[Yi], Ren, S.[Shuang],
S3U-PVNet: Arbitrary-scale point cloud upsampling via Point-Voxel Network based on Siamese Self-Supervised Learning,
CVIU(239), 2024, pp. 103890.
Elsevier DOI 2402
Self-supervised learning, Point cloud upsampling, Point-voxel method, Siamese network BibRef

Li, T.Y.[Tian-Yu], Lin, Y.[Yanghong], Cheng, B.[Bo], Ai, G.[Guo], Yang, J.[Jian], Fang, L.[Li],
PU-CTG: A Point Cloud Upsampling Network Using Transformer Fusion and GRU Correction,
RS(16), No. 3, 2024, pp. 450.
DOI Link 2402
BibRef

Lim, S.[Sangwon], El-Basyouny, K.[Karim], Yang, Y.H.[Yee Hong],
PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit Surface,
ITS(25), No. 10, October 2024, pp. 14600-14610.
IEEE DOI 2410
Point cloud compression, Laser radar, Training, Task analysis, Shape, Superresolution, Point cloud, upsampling, 3D reconstruction, LiDAR, neural implicit surface BibRef

Lan, H.[Hui], Jung, C.[Cheolkon],
DSRNet: Depth Super-Resolution Network guided by blurry depth and clear intensity edges,
SP:IC(121), 2024, pp. 117064.
Elsevier DOI Code:
WWW Link. 2401
Depth super-resolution, Convolutional neural network, Depth upsampling, Multiscale guidance, Residual estimation BibRef

Qu, W.T.[Wen-Tao], Shao, Y.[Yuantian], Meng, L.[Lingwu], Huang, X.S.[Xiao-Shui], Xiao, L.[Liang],
A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling,
CVPR24(20786-20795)
IEEE DOI 2410
Point cloud compression, Training, Geometry, Noise, Diffusion models, Feature extraction, Point Cloud Upsampling, Diffusion Model BibRef

Rong, Y.[Yi], Zhou, H.R.[Hao-Ran], Xia, K.[Kang], Mei, C.[Cheng], Wang, J.H.[Jia-Hao], Lu, T.[Tong],
RepKPU: Point Cloud Upsampling with Kernel Point Representation and Deformation,
CVPR24(21050-21060)
IEEE DOI Code:
WWW Link. 2410
Point cloud compression, Geometry, Codes, Deformation, Shape, Benchmark testing BibRef

Liu, Y.Z.[Yan-Zhe], Chen, R.[Rong], Li, Y.S.[Yu-Shi], Li, Y.X.[Yi-Xi], Tan, X.[Xuehou],
SPU-PMD: Self-Supervised Point Cloud Upsampling via Progressive Mesh Deformation,
CVPR24(5188-5197)
IEEE DOI Code:
WWW Link. 2410
Point cloud compression, Measurement, Surface reconstruction, Deformation, Network topology, Computational modeling, transformer BibRef

Yang, B.[Bin], Pfreundschuh, P.[Patrick], Siegwart, R.[Roland], Hutter, M.[Marco], Moghadam, P.[Peyman], Patil, V.[Vaishakh],
TULIP: Transformer for Upsampling of LiDAR Point Clouds,
CVPR24(15354-15364)
IEEE DOI Code:
WWW Link. 2410
Point cloud compression, Measurement, Geometry, Laser radar, Superresolution, Transforms BibRef

Dell'Eva, A.[Anthony], Orsingher, M.[Marco], Bertozzi, M.[Massimo],
Arbitrary Point Cloud Upsampling with Spherical Mixture of Gaussians,
3DV22(465-474)
IEEE DOI 2408
Point cloud compression, Training, Solid modeling, Codes, Transformers, Data models, Point Cloud Upsampling, Transformer, Gaussian Mixture Model BibRef

Liu, S.Y.[Shi-Yu], Dai, W.R.[Wen-Rui], Li, C.L.[Cheng-Lin], Zou, J.[Junni], Xiong, H.K.[Hong-Kai],
Point Cloud Upsampling with Dynamic Graph Scattering Transform,
ICIP23(3274-3278)
IEEE DOI 2312
BibRef

He, Y.[Yun], Tang, D.H.[Dan-Hang], Zhang, Y.[Yinda], Xue, X.Y.[Xiang-Yang], Fu, Y.W.[Yan-Wei],
Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions,
CVPR23(5354-5363)
IEEE DOI 2309
BibRef

Liu, Z.S.[Zhi-Song], Wang, Z.[Zijia], Jia, Z.[Zhen],
Arbitrary Point Cloud Upsampling Via Dual Back-Projection Network,
ICIP23(1470-1474)
IEEE DOI 2312
BibRef

Kumbar, A.[Akash], Anvekar, T.[Tejas], Vikrama, T.A.[Tulasi Amitha], Tabib, R.A.[Ramesh Ashok], Mudenagudi, U.[Uma],
TP-NoDe: Topology-aware Progressive Noising and Denoising of Point Clouds towards Upsampling,
WiCV-ICCV23(2264-2274)
IEEE DOI Code:
WWW Link. 2401
BibRef

Kumbar, A.[Akash], Anvekar, T.[Tejas], Tabib, R.A.[Ramesh Ashok], Mudenagudi, U.[Uma],
ASUR3D: Arbitrary Scale Upsampling and Refinement of 3D Point Clouds using Local Occupancy Fields,
eHeritage23(1644-1653)
IEEE DOI Code:
WWW Link. 2401
BibRef

Feng, W.Q.[Wan-Quan], Li, J.[Jin], Cai, H.R.[Hong-Rui], Luo, X.N.[Xiao-Nan], Zhang, J.[Juyong],
Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsampling,
CVPR22(18612-18621)
IEEE DOI 2210
Point cloud compression, Codes, Shape, Feature extraction, Robustness, Vision + graphics, Low-level vision, RGBD sensors and analytics BibRef

Wang, Y.R.[Ying-Rui], Wang, S.[Suyu], Sun, L.H.[Long-Hua],
Point Cloud Upsampling via a Coarse-to-Fine Network,
MMMod22(I:467-478).
Springer DOI 2203
BibRef

Qiu, S.[Shi], Anwar, S.[Saeed], Barnes, N.M.[Nick M.],
PU-Transformer: Point Cloud Upsampling Transformer,
ACCV22(I:326-343).
Springer DOI 2307
BibRef

Du, H.[Hang], Yan, X.J.[Xue-Jun], Wang, J.J.[Jing-Jing], Xie, D.[Di], Pu, S.L.[Shi-Liang],
Point Cloud Upsampling via Cascaded Refinement Network,
ACCV22(I:106-122).
Springer DOI 2307
BibRef

Heimann, V.[Viktoria], Spruck, A.[Andreas], Kaup, A.[André],
Frequency-Selective Geometry Upsampling of Point Clouds,
ICIP22(1511-1515)
IEEE DOI 2211
Point cloud compression, Geometry, Visualization, Image color analysis, Frequency estimation, frequency selectivity BibRef

Luo, L.Q.[Lu-Qing], Tang, L.[Lulu], Zhou, W.Y.[Wan-Yi], Wang, S.Z.[Shi-Zheng], Yang, Z.X.[Zhi-Xin],
PU-EVA: An Edge-Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling,
ICCV21(16188-16197)
IEEE DOI 2203
Point cloud compression, Training, Surface reconstruction, Semantics, Linear approximation, Network architecture, 3D from multiview and other sensors BibRef

Li, R.H.[Rui-Hui], Li, X.Z.[Xian-Zhi], Heng, P.A.[Pheng-Ann], Fu, C.W.[Chi-Wing],
Point Cloud Upsampling via Disentangled Refinement,
CVPR21(344-353)
IEEE DOI 2111
Surface reconstruction, Pipelines, Generators, Surface roughness, Rough surfaces BibRef

Qian, Y.[Yue], Hou, J.H.[Jun-Hui], Kwong, S.[Sam], He, Y.[Ying],
Pugeo-net: A Geometry-centric Network for 3d Point Cloud Upsampling,
ECCV20(XIX:752-769).
Springer DOI 2011
BibRef

Li, R., Li, X., Fu, C., Cohen-Or, D., Heng, P.,
PU-GAN: A Point Cloud Upsampling Adversarial Network,
ICCV19(7202-7211)
IEEE DOI 2004
feature extraction, image reconstruction, neural nets, latent space, upsample points, working GAN network, Network architecture BibRef

Wang, Y.F.[Yi-Fan], Wu, S.H.[Shi-Hao], Huang, H.[Hui], Cohen-Or, D.[Daniel], Sorkine-Hornung, O.[Olga],
Patch-Based Progressive 3D Point Set Upsampling,
CVPR19(5951-5960).
IEEE DOI 2002
BibRef

Yu, L., Li, X., Fu, C., Cohen-Or, D., Heng, P.,
PU-Net: Point Cloud Upsampling Network,
CVPR18(2790-2799)
IEEE DOI 1812
Feature extraction, Convolution, Geometry, Training, Surface reconstruction, Image reconstruction BibRef

Tsuchiya, A., Sugimura, D., Hamamoto, T.,
Depth upsampling by depth prediction,
ICIP17(1662-1666)
IEEE DOI 1803
Cameras, Color, DH-HEMTs, Estimation, Image color analysis, Image sequences, Motion estimation, Depth prediction, Spatio-temporal coherency BibRef

Konno, Y., Tanaka, M., Okutomi, M., Yanagawa, Y., Kinoshita, K., Kawade, M.,
Depth map upsampling by self-guided residual interpolation,
ICPR16(1394-1399)
IEEE DOI 1705
Algorithm design and analysis, Art, Estimation, Image resolution, Indexes, Interpolation, Sensors BibRef

Dong, Y., Lin, C., Zhao, Y., Yao, C., Hou, J.,
Depth map up-sampling with texture edge feature via sparse representation,
VCIP16(1-4)
IEEE DOI 1701
Color BibRef

Schneider, N.[Nick], Schneider, L.[Lukas], Pinggera, P.[Peter], Franke, U.[Uwe], Pollefeys, M.[Marc], Stiller, C.[Christoph],
Semantically Guided Depth Upsampling,
GCPR16(37-48).
Springer DOI 1611
BibRef

Fukushima, N., Takeuchi, K., Kojima, A.,
Self-similarity matching with predictive linear upsampling for depth map,
3DTV-CON16(1-4)
IEEE DOI 1610
edge detection BibRef

Krishnamurthy, S., Ramakrishnan, K.R.,
Image-guided depth map upsampling using normalized cuts-based segmentation and smoothness priors,
ICIP16(554-558)
IEEE DOI 1610
Color BibRef

Liu, W., Chen, X., Yang, J., Wu, Q.,
Robust weighted least squares for guided depth upsampling,
ICIP16(559-563)
IEEE DOI 1610
Color BibRef

Riegler, G.[Gernot], Ranftl, R.[René], Rüther, M.[Matthias], Pock, T.[Thomas], Bischof, H.[Horst],
Depth Restoration via Joint Training of a Global Regression Model and CNNs,
BMVC15(xx-yy).
DOI Link 1601
Denoising and upscaling of depth maps. BibRef

Kim, Y.J.[Young-Jung], Choi, S.[Sunghwan], Oh, C.[Changjae], Sohn, K.H.[Kwang-Hoon],
A majorize-minimize approach for high-quality depth upsampling,
ICIP15(392-396)
IEEE DOI 1512
Depth map upsampling BibRef

Zuo, Y.F.[Yi-Fan], An, P.[Ping], Zheng, S.[Shuai], Zhang, Z.Y.[Zhao-Yang],
Depth upsampling method via Markov random fields without edge-misaligned artifacts,
ICIP15(2324-2328)
IEEE DOI 1512
Markov Random Field (MRF), depth map upsampling, depth recovery BibRef

Schedl, D.C., Birklbauer, C., Bimber, O.,
Directional Super-Resolution by Means of Coded Sampling and Guided Upsampling,
ICCP15(1-10)
IEEE DOI 1511
cameras BibRef

Gong, X.J.[Xiao-Jin], Ren, J.Q.[Jian-Qiang], Lai, B.S.[Bai-Sheng], Yan, C.H.[Chao-Hua], Qian, H.[Hui],
Guided Depth Upsampling via a Cosparse Analysis Model,
FusionOutdoor14(738-745)
IEEE DOI 1409
Guided depth upsampling BibRef

Dai, L.Q.[Long-Quan], Wang, H.X.[Hao-Xing], Mei, X.[Xing], Zhang, X.P.[Xiao-Peng],
Depth Map Upsampling via Compressive Sensing,
ACPR13(90-94)
IEEE DOI 1408
compressed sensing BibRef

Ferstl, D.[David], Reinbacher, C.[Christian], Ranftl, R.[Rene], Ruether, M.[Matthias], Bischof, H.[Horst],
Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation,
ICCV13(993-1000)
IEEE DOI 1403
anisotropic tensor BibRef

Liu, M.Y.[Ming-Yu], Tuzel, O.[Oncel], Taguchi, Y.[Yuichi],
Joint Geodesic Upsampling of Depth Images,
CVPR13(169-176)
IEEE DOI 1309
depth, filtering, geodesic, upsampling BibRef

Schwarz, S.[Sebastian], Sjostrom, M.[Marten], Olsson, R.[Roger],
Incremental depth upscaling using an edge weighted optimization concept,
3DTV12(1-4).
IEEE DOI 1212
BibRef

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Creating Super Resolution Image from Video .