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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 152 papers

TitleStatusHype
Learning to Segment Rigid Motions from Two FramesCode1
PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point CloudsCode1
RAFT-3D: Scene Flow using Rigid-Motion EmbeddingsCode1
Adversarial Self-Supervised Scene Flow EstimationCode1
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
AANet: Adaptive Aggregation Network for Efficient Stereo MatchingCode1
Self-Supervised Monocular Scene Flow EstimationCode1
PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point CloudsCode1
MeteorNet: Deep Learning on Dynamic 3D Point Cloud SequencesCode1
HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point CloudsCode1
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow EstimationCode1
MoVieS: Motion-Aware 4D Dynamic View Synthesis in One Second—0
KDMOS:Knowledge Distillation for Motion SegmentationCode0
VoxelSplat: Dynamic Gaussian Splatting as an Effective Loss for Occupancy and Flow Prediction—0
Estimating Scene Flow in Robot Surroundings with Distributed Miniaturized Time-of-Flight Sensors—0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments—0
Toward Scalable, Flexible Scene Flow for Point Clouds—0
TARS: Traffic-Aware Radar Scene Flow Estimation—0
Floxels: Fast Unsupervised Voxel Based Scene Flow Estimation—0
HiMo: High-Speed Objects Motion Compensation in Point Clouds—0
SSF: Sparse Long-Range Scene Flow for Autonomous DrivingCode0
Zero-Shot Monocular Scene Flow Estimation in the Wild—0
FlowMamba: Learning Point Cloud Scene Flow with Global Motion Propagation—0
BlinkVision: A Benchmark for Optical Flow, Scene Flow and Point Tracking Estimation using RGB Frames and Events—0
Self-Supervised Scene Flow Estimation with Point-Voxel Fusion and Surface Representation—0
Neural Eulerian Scene Flow Fields—0
Human Insights Driven Latent Space for Different Driving Perspectives: A Unified Encoder for Efficient Multi-Task Inference—0
EgoFlowNet: Non-Rigid Scene Flow from Point Clouds with Ego-Motion Support—0
RMS-FlowNet++: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds—0
CMU-Flownet: Exploring Point Cloud Scene Flow Estimation in Occluded Scenario—0
Self-Supervised Multi-Frame Neural Scene Flow—0
SeMoLi: What Moves Together Belongs Together—0
FedRSU: Federated Learning for Scene Flow Estimation on Roadside UnitsCode0
DirDist: A Metric for Comparing 3D Shapes Using Directional Distance FieldsCode0
OptFlow: Fast Optimization-based Scene Flow Estimation without Supervision—0
SSFlowNet: Semi-supervised Scene Flow Estimation On Point Clouds With Pseudo Label—0
Regularizing Self-supervised 3D Scene Flows with Surface Awareness and Cyclic ConsistencyCode0
Re-Evaluating LiDAR Scene Flow for Autonomous Driving—0
Exploiting Implicit Rigidity Constraints via Weight-Sharing Aggregation for Scene Flow Estimation from Point CloudsCode0
PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Clouds—0
EMR-MSF: Self-Supervised Recurrent Monocular Scene Flow Exploiting Ego-Motion Rigidity—0
IHNet: Iterative Hierarchical Network Guided by High-Resolution Estimated Information for Scene Flow EstimationCode0
Multi-Scale Bidirectional Recurrent Network with Hybrid Correlation for Point Cloud Based Scene Flow EstimationCode0
3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion—0
Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance—0
PointConvFormer: Revenge of the Point-based Convolution—0
Unsupervised Learning of 3D Scene Flow from Monocular CameraCode0
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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