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 76–100 of 152 papers

TitleStatusHype
DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation—0
Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation—0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation—0
EgoFlowNet: Non-Rigid Scene Flow from Point Clouds with Ego-Motion Support—0
EMR-MSF: Self-Supervised Recurrent Monocular Scene Flow Exploiting Ego-Motion Rigidity—0
Estimating Scene Flow in Robot Surroundings with Distributed Miniaturized Time-of-Flight Sensors—0
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding—0
Exploiting Rigidity Constraints for LiDAR Scene Flow Estimation—0
FlowMamba: Learning Point Cloud Scene Flow with Global Motion Propagation—0
FlowMOT: 3D Multi-Object Tracking by Scene Flow Association—0
FlowNet3D++: Geometric Losses For Deep Scene Flow Estimation—0
Floxels: Fast Unsupervised Voxel Based Scene Flow Estimation—0
Hierarchical Attention Learning of Scene Flow in 3D Point Clouds—0
HiMo: High-Speed Objects Motion Compensation in Point Clouds—0
Human Insights Driven Latent Space for Different Driving Perspectives: A Unified Encoder for Efficient Multi-Task Inference—0
Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation—0
Layered RGBD Scene Flow Estimation—0
Learning Scene Dynamics from Point Cloud Sequences—0
LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images—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
Multiframe Scene Flow with Piecewise Rigid Motion—0
Neural UpFlow: A Scene Flow Learning Approach to Increase the Apparent Resolution of Particle-Based Liquids—0
Object Scene Flow for Autonomous Vehicles—0
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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