Awesome

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	PST<sup>2</sup> (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	PST<sup>2</sup>	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 (PST<sup>2</sup>)	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