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

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Showing 101–150 of 152 papers

TitleStatusHype
OptFlow: Fast Optimization-based Scene Flow Estimation without Supervision—0
Optical flow and scene flow estimation: A survey—0
PointConvFormer: Revenge of the Point-based Convolution—0
PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Clouds—0
PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds—0
RAFT-MSF: Self-Supervised Monocular Scene Flow using Recurrent Optimizer—0
RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds—0
Re-Evaluating LiDAR Scene Flow for Autonomous Driving—0
ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching—0
Residual 3D Scene Flow Learning with Context-Aware Feature Extraction—0
RMS-FlowNet: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds—0
RMS-FlowNet++: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds—0
Neural Eulerian Scene Flow Fields—0
Scene Flow Estimation: A Survey—0
SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation—0
Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk—0
Self-Supervised Multi-Frame Neural Scene Flow—0
Self-Supervised Robust Scene Flow Estimation via the Alignment of Probability Density Functions—0
Self-Supervised Scene Flow Estimation with Point-Voxel Fusion and Surface Representation—0
SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments—0
SeMoLi: What Moves Together Belongs Together—0
Simultaneous Stereo Video Deblurring and Scene Flow Estimation—0
SSFlowNet: Semi-supervised Scene Flow Estimation On Point Clouds With Pseudo Label—0
TARS: Traffic-Aware Radar Scene Flow Estimation—0
Toward Scalable, Flexible Scene Flow for Point Clouds—0
UnPWC-SVDLO: Multi-SVD on PointPWC for Unsupervised Lidar Odometry—0
Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance—0
VoxelSplat: Dynamic Gaussian Splatting as an Effective Loss for Occupancy and Flow Prediction—0
Warping of Radar Data into Camera Image for Cross-Modal Supervision in Automotive Applications—0
Zero-Shot Monocular Scene Flow Estimation in the Wild—0
FedRSU: Federated Learning for Scene Flow Estimation on Roadside UnitsCode0
A Conditional Adversarial Network for Scene Flow EstimationCode0
SENSE: a Shared Encoder Network for Scene-flow EstimationCode0
Just Go with the Flow: Self-Supervised Scene Flow EstimationCode0
Regularizing Self-supervised 3D Scene Flows with Surface Awareness and Cyclic ConsistencyCode0
SceneEDNet: A Deep Learning Approach for Scene Flow EstimationCode0
Unsupervised Learning of 3D Scene Flow from Monocular CameraCode0
IHNet: Iterative Hierarchical Network Guided by High-Resolution Estimated Information for Scene Flow EstimationCode0
Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic UnderstandingCode0
RCP: Recurrent Closest Point for Point CloudCode0
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow EstimationCode0
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow EstimationCode0
Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point CloudsCode0
SSF: Sparse Long-Range Scene Flow for Autonomous DrivingCode0
Occlusion Guided Scene Flow Estimation on 3D Point CloudsCode0
Multi-Scale Bidirectional Recurrent Network with Hybrid Correlation for Point Cloud Based Scene Flow EstimationCode0
DirDist: A Metric for Comparing 3D Shapes Using Directional Distance FieldsCode0
Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field EstimationCode0
FlowStep3D: Model Unrolling for Self-Supervised Scene Flow EstimationCode0
Exploiting Implicit Rigidity Constraints via Weight-Sharing Aggregation for Scene Flow Estimation from Point CloudsCode0
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All datasetsArgoverse 2SpringKITTI 2015 Scene Flow TestKITTI 2015 Scene Flow Training

Benchmark Results

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