11.2.4.5 Point Cloud Processing for Neural Networks, Convolutional Neural Networks

Chapter Contents (Back)
CNN. Neural Networks. Point Cloud Processing.

Yang, Z.S.[Zhi-Shuang], Jiang, W.S.[Wan-Shou], Xu, B.[Bo], Zhu, Q.S.[Quan-Sheng], Jiang, S.[San], Huang, W.[Wei],
A Convolutional Neural Network-Based 3D Semantic Labeling Method for ALS Point Clouds,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Fan, H.[Heng], Mei, X.[Xue], Prokhorov, D.[Danil], Ling, H.B.[Hai-Bin],
Multi-Level Contextual RNNs With Attention Model for Scene Labeling,
ITS(19), No. 11, November 2018, pp. 3475-3485.
IEEE DOI 1812
BibRef
Earlier:
RGB-D Scene Labeling with Multimodal Recurrent Neural Networks,
PBVS17(203-211)
IEEE DOI 1709
convolution, feedforward neural nets, image classification, recurrent neural nets, multilevel contextual RNNs, intelligent transportation system. Computational modeling, Correlation, Feature extraction, Labeling, Recurrent neural networks, Semantics BibRef

Fan, H.[Heng], Chu, P.[Peng], Latecki, L.J.[Longin Jan], Ling, H.B.[Hai-Bin],
Scene Parsing Via Dense Recurrent Neural Networks With Attentional Selection,
WACV19(1816-1825)
IEEE DOI 1904
convolutional neural nets, image annotation, recurrent neural nets, convolutional neural networks, Roads BibRef

Han, J.W.[Jun-Wei], Chen, H.[Hao], Liu, N.[Nian], Yan, C.G.[Cheng-Gang], Li, X.L.[Xue-Long],
CNNs-Based RGB-D Saliency Detection via Cross-View Transfer and Multiview Fusion,
Cyber(48), No. 11, November 2018, pp. 3171-3183.
IEEE DOI 1810
Object detection, Image color analysis, Adaptation models, Fuses, Biological neural networks, salient object detection BibRef

Chen, H.[Hao], Li, Y.F.[You-Fu], Su, D.[Dan],
Multi-modal fusion network with multi-scale multi-path and cross-modal interactions for RGB-D salient object detection,
PR(86), 2019, pp. 376-385.
Elsevier DOI 1811
BibRef
Earlier:
RGB-D Saliency Detection by Multi-stream Late Fusion Network,
CVS17(459-468).
Springer DOI 1711
RGB-D, Convolutional neural networks, Multi-path, Saliency detection BibRef

Shao, Z.P.[Zhan-Peng], Hu, Z.Y.[Zhong-Yan], Yang, J.Y.[Jian-Yu], Li, Y.F.[You-Fu],
Multi-stream feature refinement network for human object interaction detection,
JVCIR(86), 2022, pp. 103529.
Elsevier DOI 2206
Human object interaction, Action recognition, Feature refinement and learning, Multi-stream neural network, Object semantic information BibRef

Chen, H.[Hao], Li, Y.F.[You-Fu],
Three-Stream Attention-Aware Network for RGB-D Salient Object Detection,
IP(28), No. 6, June 2019, pp. 2825-2835.
IEEE DOI 1905
BibRef
Earlier:
Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection,
CVPR18(3051-3060)
IEEE DOI 1812
convolutional neural nets, feature extraction, feature selection, image colour analysis, image fusion, cross-modal cross-level attention. Object detection, Fuses, Saliency detection, Task analysis, BibRef

Li, G.B.[Guan-Bin], Gan, Y.K.[Yu-Kang], Wu, H.J.[He-Jun], Xiao, N.[Nong], Lin, L.[Liang],
Cross-Modal Attentional Context Learning for RGB-D Object Detection,
IP(28), No. 4, April 2019, pp. 1591-1601.
IEEE DOI 1901
feature extraction, image colour analysis, image representation, learning (artificial intelligence), convolutional neural network BibRef

Chen, C., Huang, H., Chen, C., Zheng, Z., Cheng, H.,
Multi-Scale Guided Mask Refinement for Coarse-to-Fine RGB-D Perception,
SPLetters(26), No. 2, February 2019, pp. 217-221.
IEEE DOI 1902
image colour analysis, image segmentation, neural nets, object detection, depth assisted methods, edge-preserving filtering BibRef

Ouadiay, F.Z.[Fatima Zahra], Zrira, N.[Nabila], Hannat, M.[Mohamed], Bouyakhf, E.[El_Houssine], Himmi, M.M.[Majid Mohamed],
3D object classification based on deep belief networks and point clouds,
IJCVR(9), No. 6, 2019, pp. 527-558.
DOI Link 1912
BibRef

Ouadiay, F.Z.[Fatima Zahra], Bouftaih, H., Bouyakhf, E.[El_Houssine], Himmi, M.M.[Majid Mohamed],
Simultaneous object detection and localization using convolutional neural networks,
ISCV18(1-8)
IEEE DOI 1807
convolution, feature extraction, feedforward neural nets, image classification, object detection BibRef

Wen, X., Han, Z., Liu, X., Liu, Y.S.,
Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds Using Spatial-Aware Capsules,
IP(29), 2020, pp. 8855-8869.
IEEE DOI 2009
Feature extraction, Shape, Routing, Aggregates, Machine learning, Spatial resolution, Point cloud, capsule network BibRef

Thermos, S.[Spyridon], Papadopoulos, G.T.[Georgios Th.], Daras, P.[Petros], Potamianos, G.[Gerasimos],
Deep sensorimotor learning for RGB-D object recognition,
CVIU(190), 2020, pp. 102844.
Elsevier DOI 1911
Object recognition, Sensorimotor learning, Object affordance, Convolutional neural networks, Recurrent neural networks, 3D convolutions BibRef

Zhou, F.[Feng], Hu, Y.[Yong], Shen, X.[Xukun],
MSANet: multimodal self-augmentation and adversarial network for RGB-D object recognition,
VC(35), No. 11, November 2018, pp. 1583-1594.
WWW Link. 1911
BibRef

Lin, Z.H.[Zhi-Hao], Huang, S.Y.[Sheng-Yu], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Learning of 3D Graph Convolution Networks for Point Cloud Analysis,
PAMI(44), No. 8, August 2022, pp. 4212-4224.
IEEE DOI 2207
Feature extraction, Convolution, Kernel, Shape, Task analysis, 3D vision, point clouds, deformable kernels, 3D segmentation BibRef

Su, F.G.[Feng-Guang], Lin, C.S.[Ci-Siang], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Learning Interpretable Representation for 3D Point Clouds,
ICPR21(7470-7477)
IEEE DOI 2105
Deep learning, Training, Solid modeling, Visualization, Shape, Transforms BibRef


Yang, Y.Q.[Yu-Qi], Wang, P.S.[Peng-Shuai], Liu, Y.[Yang],
Interpolation-Aware Padding for 3D Sparse Convolutional Neural Networks,
ICCV21(7447-7455)
IEEE DOI 2203
Training, Interpolation, Runtime, Convolution, Semantics, Segmentation, grouping and shape, Detection and localization in 2D and 3D, Machine learning architectures and formulations BibRef

Mazur, K.[Kirill], Lempitsky, V.[Victor],
Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks,
ICCV21(10695-10704)
IEEE DOI 2203
Point cloud compression, Geometry, Image segmentation, Head, Semantics, Computer architecture, Transformers, Recognition and classification BibRef

Chen, C.[Chao], Han, Z.Z.[Zhi-Zhong], Liu, Y.S.[Yu-Shen], Zwicker, M.[Matthias],
Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projection Matching,
ICCV21(12446-12457)
IEEE DOI 2203
Point cloud compression, Deep learning, Image resolution, Shape, Neural networks, Sampling methods, 3D from multiview and other sensors BibRef

Meng, Z.H.[Zi-Hang], Singh, V.[Vikas], Ravi, S.N.[Sathya N.],
Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators,
ICCV21(11615-11624)
IEEE DOI 2203
Point cloud compression, Computational modeling, Stochastic processes, Differential equations, Representation learning BibRef

Chen, Z.[Zhang], Zhang, Y.[Yinda], Genova, K.[Kyle], Fanello, S.[Sean], Bouaziz, S.[Sofien], Häne, C.[Christian], Du, R.[Ruofei], Keskin, C.[Cem], Funkhouser, T.[Thomas], Tang, D.H.[Dan-Hang],
Multiresolution Deep Implicit Functions for 3D Shape Representation,
ICCV21(13067-13076)
IEEE DOI 2203
Geometry, Point cloud compression, Solid modeling, Shape, Superresolution, Decoding, BibRef

Ran, H.X.[Hao-Xi], Zhuo, W.[Wei], Liu, J.[Jun], Lu, L.[Li],
Learning Inner-Group Relations on Point Clouds,
ICCV21(15457-15467)
IEEE DOI 2203
Point cloud compression, Analytical models, Philosophical considerations, Computational modeling, Semantics, grouping and shape BibRef

Li, F.[Feiran], Fujiwara, K.[Kent], Okura, F.[Fumio], Matsushita, Y.[Yasuyuki],
A Closer Look at Rotation-invariant Deep Point Cloud Analysis,
ICCV21(16198-16207)
IEEE DOI 2203
Point cloud compression, Task analysis, Principal component analysis, Scene analysis and understanding, Recognition and classification BibRef

Tan, H.X.[Han-Xiao], Kotthaus, H.[Helena],
Surrogate Model-Based Explainability Methods for Point Cloud NNs,
WACV22(2927-2936)
IEEE DOI 2202
Point cloud compression, Training, Deep learning, Market research, Privacy and Ethics in Vision 3D Computer Vision BibRef

Wu, S.C.[Shun-Cheng], Wald, J.[Johanna], Tateno, K.[Keisuke], Navab, N.[Nassir], Tombari, F.[Federico],
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences,
CVPR21(7511-7521)
IEEE DOI 2111
Simultaneous localization and mapping, Fuses, Semantics, Graph neural networks BibRef

Wu, Z.W.[Zong-Wei], Allibert, G.[Guillaume], Stolz, C.[Christophe], Demonceaux, C.[Cédric],
Depth-adapted CNN for RGB-D Cameras,
ACCV20(IV:388-404).
Springer DOI 2103
BibRef

Griffiths, D.[David], Boehm, J.[Jan], Ritschel, T.[Tobias],
Curiosity-driven 3D Object Detection Without Labels,
3DV21(525-534)
IEEE DOI 2201
Training, Visualization, Neural networks, Object detection, Rendering (computer graphics), Task analysis BibRef

Chen, Z.[Zhuo], Guan, T.[Tao], Luo, Y.[Yawei], Wang, Y.S.[Yue-Song], Luo, K.[Keyang], Xu, L.Y.[Luo-Yuan],
PC-Net: A Deep Network for 3D Point Clouds Analysis,
ICPR21(465-472)
IEEE DOI 2105
NN approach with non-regular data. Deep learning, Aggregates, Neural networks, Benchmark testing, Feature extraction, Pattern recognition BibRef

Li, Y.[Yawei], Chen, H.[He], Cui, Z.P.[Zhao-Peng], Timofte, R.[Radu], Pollefeys, M.[Marc], Chirikjian, G.[Gregory], Van Gool, L.J.[Luc J.],
Towards Efficient Graph Convolutional Networks for Point Cloud Handling,
ICCV21(3732-3742)
IEEE DOI 2203
Point cloud compression, Convolution, Memory management, Neural networks, Graphics processing units, Machine learning architectures and formulations BibRef

Zhou, H.R.[Hao-Ran], Feng, Y.[Yidan], Fang, M.S.[Ming-Sheng], Wei, M.Q.[Ming-Qiang], Qin, J.[Jing], Lu, T.[Tong],
Adaptive Graph Convolution for Point Cloud Analysis,
ICCV21(4945-4954)
IEEE DOI 2203
Point cloud compression, Representation learning, Convolution, Shape, Semantics, Vision applications and systems, 3D from multiview and other sensors BibRef

Wong, C.C.[Chi-Chong], Vong, C.M.[Chi-Man],
Persistent Homology based Graph Convolution Network for Fine-grained 3D Shape Segmentation,
ICCV21(7078-7087)
IEEE DOI 2203
Deep learning, Point cloud compression, Solid modeling, Convolution, Computational modeling, Semantics, Segmentation, BibRef

Kaul, C.[Chaitanya], Pears, N.[Nick], Manandhar, S.[Suresh],
FatNet: A Feature-attentive Network for 3D Point Cloud Processing,
ICPR21(7211-7218)
IEEE DOI 2105
Deep learning, Pipelines, Neural networks, Benchmark testing, Fats BibRef

Shen, W.[Wen], Wei, Z.H.[Zhi-Hua], Huang, S.[Shikun], Zhang, B.B.[Bin-Bin], Chen, P.Y.[Pan-Yue], Zhao, P.[Ping], Zhang, Q.S.[Quan-Shi],
Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing,
CVPR21(10698-10707)
IEEE DOI 2111
Measurement, Deep learning, Uncertainty, Computer architecture, Network architecture BibRef

Wang, J.[Jianren], Gang, H.[Haiming], Ancha, S.[Siddarth], Chen, Y.T.[Yi-Ting], Held, D.[David],
Semi-supervised 3D Object Detection via Temporal Graph Neural Networks,
3DV21(413-422)
IEEE DOI 2201
Training, Point cloud compression, Smoothing methods, Detectors, Object detection, Semisupervised learning, 3d object detection BibRef

Lu, X.L.[Xiao-Long], Liu, B.[Baodi], Liu, W.F.[Wei-Feng], Zhang, K.[Kai], Li, Y.[Ye], Lu, X.P.[Xiao-Ping],
Linked Attention-Based Dynamic Graph Convolution Module for Point Cloud Classification,
ICIP21(3153-3157)
IEEE DOI 2201
Convolution, Benchmark testing, Feature extraction, Real-time systems, Data mining, Point cloud data, attention module, classification BibRef

Tailor, S.A.[Shyam A.], de Jong, R.[René], Azevedo, T.[Tiago], Mattina, M.[Matthew], Maji, P.[Partha],
Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification,
DLGC21(2095-2104)
IEEE DOI 2112
Performance evaluation, Degradation, Memory management, Mixed reality, Feature extraction, Transformers BibRef

Qian, G.C.[Guo-Cheng], Abualshour, A.[Abdulellah], Li, G.H.[Guo-Hao], Thabet, A.[Ali], Ghanem, B.[Bernard],
PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks,
CVPR21(11678-11687)
IEEE DOI 2111
Convolutional codes, Pipelines, Performance gain, Feature extraction, Pattern recognition BibRef

Zhao, Y.H.[Yong-Heng], Birdal, T.[Tolga], Lenssen, J.E.[Jan Eric], Menegatti, E.[Emanuele], Guibas, L.J.[Leonidas J.], Tombari, F.[Federico],
Quaternion Equivariant Capsule Networks for 3d Point Clouds,
ECCV20(I:1-19).
Springer DOI 2011
BibRef

Thomas, H.,
Rotation-Invariant Point Convolution With Multiple Equivariant Alignments.,
3DV20(504-513)
IEEE DOI 2102
Convolution, Kernel, Standards, Deep learning, Shape, Neural networks, Deep Learning, Point Clouds, Convolution BibRef

Xu, Q.G.[Qian-Geng], Sun, X.D.[Xu-Dong], Wu, C.Y.[Cho-Ying], Wang, P.Q.[Pan-Qu], Neumann, U.[Ulrich],
Grid-GCN for Fast and Scalable Point Cloud Learning,
CVPR20(5660-5669)
IEEE DOI 2008
Computational modeling, Data models, Convolution, Aggregates, Task analysis, Feature extraction BibRef

Lin, Z., Huang, S., Wang, Y.F.,
Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis,
CVPR20(1797-1806)
IEEE DOI 2008
Convolution, Kernel, Feature extraction, Shape, Task analysis BibRef

Ye, M., Xu, S., Cao, T.,
HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection,
CVPR20(1628-1637)
IEEE DOI 2008
Feature extraction, Object detection, Laser radar, Encoding, Aggregates BibRef

Shi, W., Rajkumar, R.,
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud,
CVPR20(1708-1716)
IEEE DOI 2008
Neural networks, Object detection, Feature extraction, Convolution, Laser radar, Shape BibRef

Griffiths, D., Boehm, J.,
Weighted Point Cloud Augmentation for Neural Network Training Data Class-imbalance,
Laser19(981-987).
DOI Link 1912
BibRef

Shu, D.W.[Dong-Wook], Park, S.W.[Sung Woo], Kwon, J.[Junseok],
3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions,
ICCV19(3858-3867)
IEEE DOI 2004
Generate 3D data. feature extraction, image classification, image matching, object detection, trees (mathematics), BibRef

Zhang, L.[Ling], Zhu, Z.G.[Zhi-Gang],
Unsupervised Feature Learning for Point Cloud Understanding by Contrasting and Clustering Using Graph Convolutional Neural Networks,
3DV19(395-404)
IEEE DOI 1911
Task analysis, Feature extraction, Training, Unsupervised learning, Semantics, Graph convolutional neural network BibRef

Komarichev, A.[Artem], Zhong, Z.[Zichun], Hua, J.[Jing],
A-CNN: Annularly Convolutional Neural Networks on Point Clouds,
CVPR19(7413-7422).
IEEE DOI 2002
BibRef

Zhao, W., Yi, R., Liu, Y.,
An Adaptive Filter for Deep Learning Networks on Large-Scale Point Cloud,
ICIP19(1620-1624)
IEEE DOI 1910
Large-scale point cloud filtering, super-points, deep learning BibRef

Hansen, L.[Lasse], Diesel, J.[Jasper], Heinrich, M.P.[Mattias P.],
Multi-kernel Diffusion CNNs for Graph-Based Learning on Point Clouds,
DeepLearn-G18(III:456-469).
Springer DOI 1905
BibRef

Ye, X.Q.[Xiao-Qing], Li, J.[Jiamao], Huang, H.[Hexiao], Du, L.[Liang], Zhang, X.L.[Xiao-Lin],
3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation,
ECCV18(VII: 415-430).
Springer DOI 1810
BibRef

Gadelha, M.[Matheus], Wang, R.[Rui], Maji, S.[Subhransu],
Multiresolution Tree Networks for 3D Point Cloud Processing,
ECCV18(VII: 105-122).
Springer DOI 1810
BibRef

Zia, S., Yüksel, B., Yüret, D., Yemez, Y.,
RGB-D Object Recognition Using Deep Convolutional Neural Networks,
DeepLearn-G17(887-894)
IEEE DOI 1802
Feature extraction, Image color analysis, Object recognition, BibRef

Zhao, L.J.[Li-Jun], Liang, J.[Jie], Bai, H.H.[Hui-Hui], Wang, A.H.[An-Hong], Zhao, Y.[Yao],
Convolutional Neural Network-Based Depth Image Artifact Removal,
ICIP17(2438-2442)
IEEE DOI 1803
Color, Feature extraction, Image coding, Image color analysis, Image resolution, Training, joint filtering BibRef

Pang, G.[Guan], Neumann, U.[Ulrich],
3D point cloud object detection with multi-view convolutional neural network,
ICPR16(585-590)
IEEE DOI 1705
Complexity theory, Detectors, Object detection, Search problems, Training. 3D object extraction from point clouds. BibRef

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


Last update:Sep 1, 2022 at 11:00:56