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 91100 of 212 papers

TitleStatusHype
Self-supervised Video Object Segmentation by Motion Grouping0
Deep Learning for Robust Motion Segmentation with Non-Static Cameras0
OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous DrivingCode0
SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation0
Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes0
Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation0
Scene Flow from Point Clouds with or without Learning0
Nested Grassmannians for Dimensionality Reduction with ApplicationsCode0
Self-supervised Sparse to Dense Motion Segmentation0
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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