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
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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
Mirbauer, M.[Martin],
Krabec, M.[Miroslav],
Krivánek, J.[Jaroslav],
ikudová, E.[Elena],
Survey and Evaluation of Neural 3D Shape Classification Approaches,
PAMI(44), No. 11, November 2022, pp. 8635-8656.
IEEE DOI
2210
Shape, Feature extraction, Solid modeling, Convolution,
Neural networks, Training, 3D shape analysis,
object recognition
BibRef
Li, Y.[Yong],
Lin, Q.[Qi],
Zhang, Z.X.[Zhen-Xin],
Zhang, L.Q.[Li-Qiang],
Chen, D.[Dong],
Shuang, F.[Feng],
MFNet: Multi-Level Feature Extraction and Fusion Network for
Large-Scale Point Cloud Classification,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Wang, B.[Bin],
Wang, H.[Hao],
Song, D.M.[Dong-Mei],
A Filtering Method for LiDAR Point Cloud Based on Multi-Scale CNN
with Attention Mechanism,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Xv, J.B.[Jia-Bin],
Deng, F.[Fei],
Liu, H.B.[Hai-Bing],
Point Cloud Convolution Network Based on Spatial Location
Correspondence,
IJGI(11), No. 12, 2022, pp. xx-yy.
DOI Link
2301
BibRef
Woo, S.[Sungmin],
Lee, D.[Dogyoon],
Hwang, S.[Sangwon],
Kim, W.J.[Woo Jin],
Lee, S.Y.[Sang-Youn],
MKConv: Multidimensional feature representation for point cloud
analysis,
PR(143), 2023, pp. 109800.
Elsevier DOI
2310
Point cloud, Feature learning, Convolutional neural network, 3D vision
BibRef
Hao, Y.[Yun],
Su, Y.K.[Yu-Kun],
Lin, G.S.[Guo-Sheng],
Su, H.J.[Han-Jing],
Wu, Q.Y.[Qing-Yao],
Contrastive Generative Network with Recursive-Loop for 3D point cloud
generalized zero-shot classification,
PR(144), 2023, pp. 109843.
Elsevier DOI
2310
3D point cloud, Generalized zero-shot, Contrastive learning, Recursive-loop
BibRef
Chen, C.C.[Chuan-Chuan],
Liu, D.R.[Dong-Rui],
Xu, C.Q.[Chang-Qing],
Truong, T.K.[Trieu-Kien],
SAKS: Sampling Adaptive Kernels From Subspace for Point Cloud Graph
Convolution,
CirSysVideo(33), No. 10, October 2023, pp. 6013-6025.
IEEE DOI
2310
BibRef
Zhao, Z.[Zhi],
Ma, Y.X.[Yan-Xin],
Xu, K.[Ke],
Wan, J.W.[Jian-Wei],
Information geometry based extreme low-bit neural network for point
cloud,
PR(146), 2024, pp. 109986.
Elsevier DOI
2311
Information geometry, Binary, Ternary, Neural network, Point cloud
BibRef
Belyaev, E.[Evgeny],
Liu, K.[Kai],
An Adaptive Binary rANS With Probability Estimation in Reverse Order,
SPLetters(30), 2023, pp. 1487-1491.
IEEE DOI
2311
Asymmetric Numeral Systems.
BibRef
Xie, T.[Tao],
Zhang, H.M.[Hao-Ming],
Yang, L.Q.[Lin-Qi],
Wang, K.[Ke],
Dai, K.[Kun],
Li, R.F.[Rui-Feng],
Zhao, L.J.[Li-Jun],
Point-NAS: A Novel Neural Architecture Search Framework for Point
Cloud Analysis,
IP(32), 2023, pp. 6526-6542.
IEEE DOI
2312
BibRef
Wang, L.[Li],
Xie, T.[Tao],
Zhang, X.Y.[Xin-Yu],
Jiang, Z.Q.[Zhi-Qiang],
Yang, L.Q.[Lin-Qi],
Zhang, H.M.[Hao-Ming],
Li, X.Y.[Xiao-Yu],
Ren, Y.L.[Yi-Long],
Yu, H.Y.[Hai-Yang],
Li, J.[Jun],
Liu, H.P.[Hua-Ping],
Auto-Points: Automatic Learning for Point Cloud Analysis with Neural
Architecture Search,
MultMed(26), 2024, pp. 2878-2893.
IEEE DOI
2402
Point cloud compression, Task analysis, Training,
Feature extraction, Computer architecture, Object detection, deep learning
BibRef
Yuan, Z.Y.[Zi-Yang],
Zhu, Y.M.[Yi-Ming],
Li, Y.[Yu],
Liu, H.Y.[Hong-Yu],
Yuan, C.[Chun],
Make Encoder Great Again in 3D GAN Inversion through Geometry and
Occlusion-Aware Encoding,
ICCV23(2437-2447)
IEEE DOI
2401
BibRef
Chen, X.Y.[Xing-Yu],
Deng, Y.[Yu],
Wang, B.Y.[Bao-Yuan],
Mimic3D: Thriving 3D-Aware GANs via 3D-to-2D Imitation,
ICCV23(2338-2348)
IEEE DOI Code:
WWW Link.
2401
BibRef
Sun, J.Q.[Jia-Qi],
Niu, D.M.[Dong-Mei],
Lv, N.[Na],
Dou, W.T.[Wen-Tao],
Peng, J.L.[Jing-Liang],
Lightweight Multi-View-Group Neural Network for 3D Shape
Classification,
ICIP23(3409-3413)
IEEE DOI
2312
Based on depth maps generated by multi-view rendering.
BibRef
Kaul, C.[Chaitanya],
Mitton, J.[Joshua],
Dai, H.[Hang],
Murray-Smith, R.[Roderick],
Convolutional Point Transformer,
ACCVWS22(308-324).
Springer DOI
2307
Process 3D points to use on NN.
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, 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.F.[Ruo-Fei],
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],
Tateno, K.[Keisuke],
Navab, N.[Nassir],
Tombari, F.[Federico],
Incremental 3D Semantic Scene Graph Prediction from RGB Sequences,
CVPR23(5064-5074)
IEEE DOI
2309
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.S.[Gregory S.],
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.K.[Shi-Kun],
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, Network architecture
BibRef
Wang, J.R.[Jian-Ren],
Gang, H.M.[Hai-Ming],
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
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 .