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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 212 papers

TitleStatusHype
Spherical formulation of geometric motion segmentation constraints in fisheye cameras—0
Self-supervised Video Object Segmentation by Motion Grouping—0
Video Class Agnostic Segmentation Benchmark for Autonomous DrivingCode1
Deep Learning for Robust Motion Segmentation with Non-Static Cameras—0
OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous DrivingCode0
SSTVOS: Sparse Spatiotemporal Transformers for Video Object SegmentationCode1
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan SynchronizationCode1
Learning to Segment Rigid Motions from Two FramesCode1
SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation—0
Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes—0
Event-based Motion Segmentation with Spatio-Temporal Graph CutsCode1
Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation—0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation—0
Scene Flow from Point Clouds with or without Learning—0
Nested Grassmannians for Dimensionality Reduction with ApplicationsCode0
Self-supervised Sparse to Dense Motion Segmentation—0
Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-task Baseline—0
Jointly learning visual motion and confidence from local patches in event cameras—0
On the Usage of the Trifocal Tensor in Motion SegmentationCode0
0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event CameraCode1
DGSAC: Density Guided Sampling and Consensus—0
Learning Visual Motion Segmentation Using Event Surfaces—0
Multi-Mutual Consistency Induced Transfer Subspace Learning for Human Motion Segmentation—0
Understanding Dynamic Scenes using Graph Convolution NetworksCode1
Is an Affine Constraint Needed for Affine Subspace Clustering?—0
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All datasetsApolloScapeHopkins155KT3DMoSegMTPV62

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rule BasedAccuracy90—Unverified
2Rel-Att-GCNAccuracy89—Unverified
3MRGCNAccuracy86—Unverified
4MRGCN-LSTMAccuracy72—Unverified
5St-RNNAccuracy63—Unverified
#ModelMetricClaimedVerifiedStatus
1SSCClassification Error2.18—Unverified
2T-LinkageClassification Error1.97—Unverified
3RSIMClassification Error1.01—Unverified
4MVCClassification Error0.31—Unverified
#ModelMetricClaimedVerifiedStatus
1MultiViewClusteringError7.92—Unverified
#ModelMetricClaimedVerifiedStatus
1MVCClassification Error0.65—Unverified