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
Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow EstimationCode1
3D Object Detection with a Self-supervised Lidar Scene Flow BackboneCode1
HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point CloudsCode1
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Diffusion ModelCode1
Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and StereoCode1
Self-Supervised Scene Flow Estimation with 4-D Automotive RadarCode1
SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and SelectionCode1
DiffSF: Diffusion Models for Scene Flow EstimationCode1
Self-Supervised Multi-Frame Monocular Scene FlowCode1
AANet: Adaptive Aggregation Network for Efficient Stereo MatchingCode1
Learning to Segment Rigid Motions from Two FramesCode1
Let-It-Flow: Simultaneous Optimization of 3D Flow and Object ClusteringCode1
Dual Task Learning by Leveraging Both Dense Correspondence and Mis-Correspondence for Robust Change Detection With Imperfect MatchesCode1
Self-Supervised Flow Estimation using Geometric Regularization with Applications to Camera Image and Grid Map Sequences—0
3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion—0
4D Temporally Coherent Light-field Video—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
BlinkVision: A Benchmark for Optical Flow, Scene Flow and Point Tracking Estimation using RGB Frames and Events—0
Bounding Boxes, Segmentations and Object Coordinates: How Important Is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?—0
CMU-Flownet: Exploring Point Cloud Scene Flow Estimation in Occluded Scenario—0
Combining Stereo Disparity and Optical Flow for Basic Scene Flow—0
Consistency Guided Scene Flow Estimation—0
Deep learning based 2.5D flow field estimation for maximum intensity projections of 4D optical coherence tomography—0
Deep Rigid Instance Scene Flow—0
Dense Scene Flow from Stereo Disparity and Optical Flow—0
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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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