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 7180 of 212 papers

TitleStatusHype
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation0
A Detailed Rubric for Motion Segmentation0
Event-based Egocentric Human Pose Estimation in Dynamic Environment0
Consistency and Diversity induced Human Motion Segmentation0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation0
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes0
Dynamics Enhanced Multi-Camera Motion Segmentation From Unsynchronized Videos0
Community Recovery in Hypergraphs0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction0
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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