- Recent papers (from 2019)
- welcome to add if any information misses. 😎- [3DV 24] Multi-Body Neural Scene Flow [2310.10301][code]
- [arXiv] Self-Supervised 3D Scene Flow Estimation and Motion Prediction using Local Rigidity Prior [2310.11284]
- [ICCV 23] DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds [2308.04383][code]
- [CVPR 23] Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision [2303.00462][code]
- [CVPR 23] SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flow [2211.14020][code]
- [CVPR 23] Self-Supervised 3D Scene Flow Estimation Guided by Superpoints [2305.02528][code]
- [arXiv] GMSF: Global Matching Scene Flow [2305.17432][code]
- [arXiv] ZeroFlow: Fast Zero Label Scene Flow via Distillation [2305.10424][code]
- [arXiv] ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds [2304.12589]
- [arXiv] Fast Neural Scene Flow [2304.09121][code]
- [arXiv] Re-Evaluating LiDAR Scene Flow for Autonomous Driving [2304.02150]
- [RAL 23] PT-FlowNet: Scene Flow Estimation on Point Clouds with Point Transformer
- [arXiv] GotFlow3D: Recurrent Graph Optimal Transport for Learning 3D Flow Motion in Particle Tracking [2210.17012]
- [arXiv] PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Clouds [2302.14193]
- [arXiv] Exploiting Implicit Rigidity Constraints via Weight-Sharing Aggregation for Scene Flow Estimation from Point Clouds [2303.02454]
- [ECCV 22] FH-Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-world Point Clouds [code]
- [RAL 22] Estimation and Propagation: Scene Flow Prediction on Occluded Point Clouds
- [arxiv] 3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion [2209.13130]
- [arXiv] PointConvFormer: Revenge of the Point-based Convolution [2208.02879]
- [ECCV 22] Dynamic 3D Scene Analysis by Point Cloud Accumulation [2207.12394] [code]
- [ECCV 22] What Matters for 3D Scene Flow Network [2207.09143] [code]
- [ECCV 22] Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation [2207.07522] [code]
- [CVPR 22] RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds [2205.11028] [code]
- [CVPR 22] Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Clouds [2203.16895] [code]
- [ICRA 22] RMS-FlowNet: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds [2204.00354]
- [AAAI 22] Self-Supervised Robust Scene Flow Estimation via the Alignment of Probability Density Functions [2203.12193]
- [IJCV] Learning Scene Dynamics from Point Cloud Sequences [2111.08755] [code]
- [RA-L&IROS 22] Self-Supervised Scene Flow Estimation with 4D Automotive Radar [2203.01137][code]
- [arXiv] Learning Scene Flow in 3D Point Clouds with Noisy Pseudo Labels [2203.12655]
- [ACM MM 22] RPPformer-Flow: Relative Position Guided Point Transformer for Scene Flow Estimation [link] [code]
- [CVPR 21] Self-Supervised Pillar Motion Learning for Autonomous Driving [2104.08683][code]
- [CVPR 21] Learning to Segment Rigid Motions from Two Frames [2101.03694][code]
- [CVPR 21 Oral] Weakly Supervised Learning of Rigid 3D Scene Flow [2102.08945][code]
- [RA-L 21] Scalable Scene Flow from Point Clouds in the Real World [2103.01306] [code]
- [CVPR 21] FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds [2104.00798] [code]
- [CVPRW 21] Occlusion Guided Scene Flow Estimation on 3D Point Clouds [2104.00798] [code]
- [3DV 21] Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point Clouds [2104.04724]
- [AAAI 22] SCTN: Sparse Convolution-Transformer Network for Scene Flow Estimation [2105.04447]
- [CVPR 21] HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding [2105.07751]
- [CVPR 21 Oral] Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk [2105.08248]
- [TIM 22] Residual 3D Scene Flow Learning with Context-Aware Feature Extraction [2109.04685]
- [NeurIPS 21] Accurate Point Cloud Registration with Robust Optimal Transport [2111.00648] [code]
- [NeurIPS 21 spotlight] Neural Scene Flow Prior [2111.01253] [code]
- [ECCV 20] PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds [1911.12408][code]
- [ECCV 20] FLOT: Scene Flow on Point Clouds Guided by Optimal Transport [2007.11142][code]
- [3DV 20] Self-Supervised Learning of Non-Rigid Residual Flow and Ego-Motion [2009.10467] [code]
- [3DV 20] Scene Flow from Point Clouds with or without Learning [2011.00320]
- [3DV 20] Adversarial Self-Supervised Scene Flow Estimation [2011.00551]
- [WACV 20] FlowNet3D++: Geometric Losses For Deep Scene Flow Estimation [1912.01438]
- [CVPR 20] Just Go With the Flow: Self-Supervised Scene Flow Estimation [1912.00497][code]
- [TIP 21] Hierarchical Attention Learning of Scene Flow in 3D Point Clouds [2010.05762]
- [CVPR 21] PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization [2012.00972]
- [CVPR 21] FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation [2011.10147]
- [CVPR 21] RAFT-3D: Scene Flow using Rigid-Motion Embeddings [2012.00726]
- [CVPR 21] PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds [2012.00987] [code]
- VoxFlowNet: Learning Scene Flow in 3D Point Clouds through Voxel Grids [code]
- [IROS 20] PillarFlowNet: A Real-time Deep Multitask Network for LiDAR-based 3D Object Detection and Scene Flow Estimation [IROS20]
- [ICCV 19] MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences [1910.09165][code]
- [CVPR 19] FlowNet3D: Learning Scene Flow in 3D Point Clouds [1806.01411][code]
- [CVPR 19] HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds [1906.05332][code]
Since there is currently no raw dataset for Scene Flow Estimation with a point cloud as input, the pioneers FlowNet3D and HPLFlowNet provide two versions of the dataset based on the raw dataset.
- FlyingThings3D [official website]
- KITTI 2015 [official website]
- Processed by FlowNet3D [code]
- Processed by HPLFlowNet [code]
There are some differences in the way FlowNet3D and HPLFlowNet process data. [FlowNet3D only provides the code to process FlyingThings3D, HPLFlowNet provides code to process FlyingThings3D and KITTI15] Some papers will compare two kinds of data at the same time. [But at the moment there seems to be more comparisons on HPLFlowNet]