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 101–150 of 152 papers

TitleStatusHype
RAFT-MSF: Self-Supervised Monocular Scene Flow using Recurrent Optimizer—0
Warping of Radar Data into Camera Image for Cross-Modal Supervision in Automotive Applications—0
RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds—0
Re-Evaluating LiDAR Scene Flow for Autonomous Driving—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—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
FlowMamba: Learning Point Cloud Scene Flow with Global Motion Propagation—0
Exploiting Rigidity Constraints for LiDAR Scene Flow Estimation—0
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding—0
Estimating Scene Flow in Robot Surroundings with Distributed Miniaturized Time-of-Flight Sensors—0
Self-Supervised Flow Estimation using Geometric Regularization with Applications to Camera Image and Grid Map Sequences—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
EMR-MSF: Self-Supervised Recurrent Monocular Scene Flow Exploiting Ego-Motion Rigidity—0
EgoFlowNet: Non-Rigid Scene Flow from Point Clouds with Ego-Motion Support—0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation—0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk—0
Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation—0
DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation—0
Dense Scene Flow from Stereo Disparity and Optical Flow—0
Deep Rigid Instance Scene Flow—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
Deep learning based 2.5D flow field estimation for maximum intensity projections of 4D optical coherence tomography—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
4D Temporally Coherent Light-field Video—0
Consistency Guided Scene Flow Estimation—0
Simultaneous Stereo Video Deblurring and Scene Flow Estimation—0
Combining Stereo Disparity and Optical Flow for Basic Scene Flow—0
SSFlowNet: Semi-supervised Scene Flow Estimation On Point Clouds With Pseudo Label—0
3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion—0
CMU-Flownet: Exploring Point Cloud Scene Flow Estimation in Occluded Scenario—0
TARS: Traffic-Aware Radar Scene Flow Estimation—0
Toward Scalable, Flexible Scene Flow for Point Clouds—0
Bounding Boxes, Segmentations and Object Coordinates: How Important Is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?—0
LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images—0
Learning Scene Dynamics from Point Cloud Sequences—0
Layered RGBD Scene Flow Estimation—0
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes—0
MoVieS: Motion-Aware 4D Dynamic View Synthesis in One Second—0
Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation—0
Multiframe Scene Flow with Piecewise Rigid Motion—0
UnPWC-SVDLO: Multi-SVD on PointPWC for Unsupervised Lidar Odometry—0
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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