SOTAVerified

Motion Segmentation

Motion Segmentation is an essential task in many applications in Computer Vision and Robotics, such as surveillance, action recognition and scene understanding. The classic way to state the problem is the following: given a set of feature points that are tracked through a sequence of images, the goal is to cluster those trajectories according to the different motions they belong to. It is assumed that the scene contains multiple objects that are moving rigidly and independently in 3D-space.

Source: Robust Motion Segmentation from Pairwise Matches

Papers

Showing 2650 of 212 papers

TitleStatusHype
Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Network0
Out of the Room: Generalizing Event-Based Dynamic Motion Segmentation for Complex Scenes0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation0
Learned Trajectory Embedding for Subspace Clustering0
WoodScape Motion Segmentation for Autonomous Driving -- CVPR 2023 OmniCV Workshop Challenge0
Appearance-Based Refinement for Object-Centric Motion Segmentation0
Un-EvMoSeg: Unsupervised Event-based Independent Motion Segmentation0
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes0
Motion2Language, unsupervised learning of synchronized semantic motion segmentationCode1
Segmenting the motion components of a video: A long-term unsupervised model0
Motion Segmentation from a Moving Monocular Camera0
RaTrack: Moving Object Detection and Tracking with 4D Radar Point CloudCode2
Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects0
A Multi-Scale Recurrent Framework for Motion Segmentation With Event CameraCode0
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving0
Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual GroupingCode1
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots0
Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal SupervisionCode1
Unsupervised Space-Time Network for Temporally-Consistent Segmentation of Multiple MotionsCode0
GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene FlowCode0
DytanVO: Joint Refinement of Visual Odometry and Motion Segmentation in Dynamic EnvironmentsCode2
ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the WildCode2
Segmenting Moving Objects via an Object-Centric Layered RepresentationCode1
Segmentation Guided Deep HDR Deghosting0
Self-Supervised Deep Subspace Clustering with Entropy-norm0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rule BasedAccuracy90Unverified
2Rel-Att-GCNAccuracy89Unverified
3MRGCNAccuracy86Unverified
4MRGCN-LSTMAccuracy72Unverified
5St-RNNAccuracy63Unverified
#ModelMetricClaimedVerifiedStatus
1SSCClassification Error2.18Unverified
2T-LinkageClassification Error1.97Unverified
3RSIMClassification Error1.01Unverified
4MVCClassification Error0.31Unverified
#ModelMetricClaimedVerifiedStatus
1MultiViewClusteringError7.92Unverified
#ModelMetricClaimedVerifiedStatus
1MVCClassification Error0.65Unverified