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 5175 of 152 papers

TitleStatusHype
Neural Scene Flow PriorCode1
ScaleFlow++: Robust and Accurate Estimation of 3D Motion from VideoCode1
GMSF: Global Matching Scene FlowCode1
Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal SupervisionCode1
Dual Task Learning by Leveraging Both Dense Correspondence and Mis-Correspondence for Robust Change Detection With Imperfect MatchesCode1
Accurate Point Cloud Registration with Robust Optimal TransportCode1
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion SensingCode1
PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point CloudsCode1
MeteorNet: Deep Learning on Dynamic 3D Point Cloud SequencesCode1
DiffSF: Diffusion Models for Scene Flow EstimationCode1
CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow EstimationCode1
Self-Supervised 3D Scene Flow Estimation and Motion Prediction using Local Rigidity PriorCode1
Learning to Segment Rigid Motions from Two FramesCode1
Just Go with the Flow: Self-Supervised Scene Flow EstimationCode0
IHNet: Iterative Hierarchical Network Guided by High-Resolution Estimated Information for Scene Flow EstimationCode0
SceneEDNet: A Deep Learning Approach for Scene Flow EstimationCode0
FlowStep3D: Model Unrolling for Self-Supervised Scene Flow EstimationCode0
FedRSU: Federated Learning for Scene Flow Estimation on Roadside UnitsCode0
DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDARCode0
Exploiting Implicit Rigidity Constraints via Weight-Sharing Aggregation for Scene Flow Estimation from Point CloudsCode0
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow EstimationCode0
RCP: Recurrent Closest Point for Point CloudCode0
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow EstimationCode0
Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic UnderstandingCode0
DirDist: A Metric for Comparing 3D Shapes Using Directional Distance FieldsCode0
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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