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 2130 of 212 papers

TitleStatusHype
Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal SupervisionCode1
HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object InteractionCode1
Discovering Objects that Can MoveCode1
0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event CameraCode1
Event-Based Motion Segmentation by Motion CompensationCode1
Motion Segmentation using Frequency Domain Transformer NetworksCode1
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan SynchronizationCode1
Learning to Segment Rigid Motions from Two FramesCode1
Segmenting Moving Objects via an Object-Centric Layered RepresentationCode1
Understanding Dynamic Scenes using Graph Convolution NetworksCode1
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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