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 121130 of 212 papers

TitleStatusHype
Semantic Motion Segmentation Using Dense CRF Formulation0
Simultaneous Localization, Mapping, and Manipulation for Unsupervised Object Discovery0
SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation0
Spherical formulation of geometric motion segmentation constraints in fisheye cameras0
SpikeMS: Deep Spiking Neural Network for Motion Segmentation0
Stereo-based Multi-motion Visual Odometry for Mobile Robots0
Structured Sparse Subspace Clustering: A Unified Optimization Framework0
Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework0
Subspace Clustering with Active Learning0
Temporally Consistent Motion Segmentation from RGB-D Video0
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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