SOTAVerified

Scene Flow Estimation

Optical flow is a two-dimensional motion field in the image plane. It is the projection of the three-dimensional motion of the world. If the world is completely non-rigid, the motions of the points in the scene may all be indepen- dent of each other. One representation of the scene motion is therefore a dense three-dimensional vector field defined for every point on every surface in the scene. By analogy with optical flow, we refer to this three-dimensional motion field as scene flow.

Source: Vedula, Sundar, et al. "Three-dimensional scene flow." IEEE transactions on pattern analysis and machine intelligence 27.3 (2005): 475-480. pdf

Papers

Showing 101125 of 152 papers

TitleStatusHype
Weakly Supervised Learning of Rigid 3D Scene FlowCode1
Optical flow and scene flow estimation: A survey0
Learning to Segment Rigid Motions from Two FramesCode1
Warping of Radar Data into Camera Image for Cross-Modal Supervision in Automotive Applications0
FlowMOT: 3D Multi-Object Tracking by Scene Flow Association0
PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point CloudsCode1
RAFT-3D: Scene Flow using Rigid-Motion EmbeddingsCode1
Occlusion Guided Scene Flow Estimation on 3D Point CloudsCode0
FlowStep3D: Model Unrolling for Self-Supervised Scene Flow EstimationCode0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation0
Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation0
Adversarial Self-Supervised Scene Flow EstimationCode1
Hierarchical Attention Learning of Scene Flow in 3D Point Clouds0
DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDARCode0
PointPWC-Net: Cost Volume on Point Clouds for (Self-)Supervised Scene Flow EstimationCode1
FLOT: Scene Flow on Point Clouds Guided by Optimal TransportCode1
LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow EstimationCode1
ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching0
Consistency Guided Scene Flow Estimation0
AANet: Adaptive Aggregation Network for Efficient Stereo MatchingCode1
Self-Supervised Monocular Scene Flow EstimationCode1
FlowNet3D++: Geometric Losses For Deep Scene Flow Estimation0
Just Go with the Flow: Self-Supervised Scene Flow EstimationCode0
PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point CloudsCode1
LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FastNSFEPE 3-Way0.11Unverified
2FastFlow3DEPE 3-Way0.06Unverified
3NSFPEPE 3-Way0.06Unverified
4ZeroFlow 5x XLEPE 3-Way0.05Unverified
5SeFlowEPE 3-Way0.05Unverified
6TrackFlowEPE 3-Way0.05Unverified
7DeFlowEPE 3-Way0.03Unverified
#ModelMetricClaimedVerifiedStatus
1CamLiFlow (K)1px total85.31Unverified
2RAFT-3D (F)1px total78.82Unverified
3M-FUSE (K)1px total62.49Unverified
4CamLiFlow (F)1px total50.08Unverified
5RAFT-3D (K)1px total37.26Unverified
6M-FUSE (F)1px total34.9Unverified
#ModelMetricClaimedVerifiedStatus
1Self-Mono-SFSF-all49.54Unverified
2Multi-Mono-SFSF-all44.04Unverified
3PWOC-3DSF-all15.69Unverified
4CamLiRAFTSF-all4.26Unverified
#ModelMetricClaimedVerifiedStatus
1Self-Mono-SFD1-all31.25Unverified
2Multi-Mono-SFD1-all27.33Unverified
3EPCD1-all26.81Unverified
4EPC++D1-all23.84Unverified