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.
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 | - | - |
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 |
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 |
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 |
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 | Spatial-Temporal Transformer for 3D Point Cloud Sequences (PST2) | WACV'22 |
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 | TPU-GAN: Learning Temporal Coherence From Dynamic Point Cloud Sequences | ICLR'22 |
10 | HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction | CVPR'22 |
11 | IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment | CVPR'22 |