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GitHub - hehefan/Awesome-Dynamic-Point-Cloud-Analysis

Awesome Dynamic Point Cloud / Point Cloud Video / Point Cloud Sequence / 4D Point Cloud Analysis

If you find any related paper, please kindly let me know. I will keep updating the page. Thanks for your valuable contribution.

For two-frame sence flow estimation, please refer to Awesome Point Cloud Scene Flow.

I. Video/Sqeuence-level Classification

1. MSR-Action3D

No. Method 4 8 12 16 20 24
1 MeteorNet 78.11 81.14 86.53 88.21 - 88.50
2 P4Transformer 80.13 83.17 87.54 89.56 90.24 90.94
3 PSTNet 81.14 83.50 87.88 89.90 - 91.20
4 SequentialPointNet 77.66 86.45 88.64 89.56 91.21 91.94
5 PSTNet++ 81.53 83.50 88.15 90.24 - 92.68
6 Anchor-Based Spatio-Temporal Attention 80.13 87.54 89.90 91.24 - 93.03
7 PST-Transformer 81.14 83.97 88.15 91.98 - 93.73
8 Kinet 79.80 83.84 88.53 91.92 - 93.27
9 PST2 (MeteorNet + STSA) 81.14 86.53 88.55 89.22 - -

2. NTU RBG+D 60

No. Method Cross Subject Cross View
1 3DV-PointNet++ 88.8 96.3
2 P4Transformer 90.2 96.4
3 PSTNet 90.5 96.5
4 PSTNet++ 91.4 96.7
5 PST-Transformer 91.0 96.4
6 SequentialPointNet 90.3 97.6
7 Kinet 92.3 96.4
8 GeometryMotion-Net 92.7 98.9
9 GeometryMotion-Transformer 93.7 99.0

3. NTU RBG+D 120

No. Method Cross Subject Cross Setup
1 3DV-PointNet++ 82.4 93.5
2 P4Transformer 86.4 93.5
3 PSTNet 87.0 93.8
4 PSTNet++ 88.6 93.8
5 PST-Transformer 87.5 94.0
6 SequentialPointNet 83.5 95.4
7 GeometryMotion-Net 90.1 93.6
8 GeometryMotion-Transformer 90.4 93.8

4. SHREC'17

No. Method Acc
1 PointLSTM (Min et al.) 94.7
2 Kinet 95.2

5. NvGesture

No. Method Acc
1 FlickerNet 86.3
2 PointLSTM (Min et al.) 87.5
3 Kinet 89.1

II. Point-level Segmentation

1. Synthia 4D

No. Method mIoU (3 frames)
1 MinkNet14 77.46
2 MeteorNet 81.80
3 PSTNet 82.24
4 PSTNet++ 82.60
5 ASAP-Net 82.73
6 P4Transformer 83.16
7 PST-Transformer 83.95
8 Anchor-Based Spatio-Temporal Attention 84.77
9 PST2 81.86

2. SemanticKITTI

No. Paper Title Venue
1 SpSequenceNet: Semantic Segmentation Network on 4D Point Clouds CVPR'20
2 LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignment 3DV'20
3 4D Panoptic LiDAR Segmentation CVPR'21
4 LiDAR-based 4D Panoptic Segmentation via Dynamic Shifting Network ICCV'21
5 Spatial-Temporal Transformer for 3D Point Cloud Sequences (PST2) WACV'22

III. Other Task

No. Paper Title Venue
1 Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net CVPR'18
2 PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing arXiv'19
3 Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics ICCV'19
4 Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds ICLR'20
5 CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations NeurIPS'20
6 Learning Scene Dynamics from Point Cloud Sequences IJCV'21
7 Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data RAL'21
8 PointINet: Point Cloud Frame Interpolation Network AAAI'21
9 Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks CoRL'21
10 TPU-GAN: Learning Temporal Coherence From Dynamic Point Cloud Sequences ICLR'22
11 HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction CVPR'22
12 IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment CVPR'22
13 Dynamic Point Cloud Compression with Cross-Sectional Approach arXiv'22
14 Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction CVPR'22
15 PointMotionNet: Point-Wise Motion Learning for Large-Scale LiDAR Point Clouds Sequences CVPR'22
16 LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds CVPR'22
17 Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding ECCV'22