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
Segmentation Guided Deep HDR Deghosting0
Self-Supervised Deep Subspace Clustering with Entropy-norm0
EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms0
Quantum Motion Segmentation0
ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation0
The Right Spin: Learning Object Motion from Rotation-Compensated Flow Fields0
Consistency and Diversity induced Human Motion Segmentation0
LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation0
Unsupervised Object Learning via Common FateCode0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation0
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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