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

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Showing 1–50 of 212 papers

TitleStatusHype
Channel-wise Motion Features for Efficient Motion Segmentation—0
Temporal Rate Reduction Clustering for Human Motion Segmentation—0
KDMOS:Knowledge Distillation for Motion SegmentationCode0
FreeGave: 3D Physics Learning from Dynamic Videos by Gaussian VelocityCode1
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras—0
Event-based Egocentric Human Pose Estimation in Dynamic Environment—0
Iterative Event-based Motion Segmentation by Variational Contrast MaximizationCode0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
BEVMOSNet: Multimodal Fusion for BEV Moving Object Segmentation—0
Wandering around: A bioinspired approach to visual attention through object motion sensitivityCode0
Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos—0
Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking—0
On Moving Object Segmentation from Monocular Video with Transformers—0
RoMo: Robust Motion Segmentation Improves Structure from Motion—0
DATAP-SfM: Dynamic-Aware Tracking Any Point for Robust Structure from Motion in the Wild—0
Human Insights Driven Latent Space for Different Driving Perspectives: A Unified Encoder for Efficient Multi-Task Inference—0
CV-MOS: A Cross-View Model for Motion SegmentationCode0
Retina-Inspired Object Motion Segmentation for Event-Cameras—0
Hardware-Algorithm Re-engineering of Retinal Circuit for Intelligent Object Motion Segmentation—0
Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach—0
Motion Segmentation for Neuromorphic Aerial SurveillanceCode0
MOD-UV: Learning Mobile Object Detectors from Unlabeled VideosCode1
Zero-Shot Monocular Motion Segmentation in the Wild by Combining Deep Learning with Geometric Motion Model Fusion—0
Turb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with Atmospheric Turbulence—0
Moving Object Segmentation: All You Need Is SAM (and Flow)Code3
Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Network—0
Out of the Room: Generalizing Event-Based Dynamic Motion Segmentation for Complex Scenes—0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation—0
Learned Trajectory Embedding for Subspace Clustering—0
WoodScape Motion Segmentation for Autonomous Driving -- CVPR 2023 OmniCV Workshop Challenge—0
Appearance-Based Refinement for Object-Centric Motion Segmentation—0
Un-EvMoSeg: Unsupervised Event-based Independent Motion Segmentation—0
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes—0
Motion2Language, unsupervised learning of synchronized semantic motion segmentationCode1
Segmenting the motion components of a video: A long-term unsupervised model—0
Motion Segmentation from a Moving Monocular Camera—0
RaTrack: Moving Object Detection and Tracking with 4D Radar Point CloudCode2
Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects—0
A Multi-Scale Recurrent Framework for Motion Segmentation With Event CameraCode0
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving—0
Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual GroupingCode1
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots—0
Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal SupervisionCode1
Unsupervised Space-Time Network for Temporally-Consistent Segmentation of Multiple MotionsCode0
GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene FlowCode0
DytanVO: Joint Refinement of Visual Odometry and Motion Segmentation in Dynamic EnvironmentsCode2
ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the WildCode2
Segmenting Moving Objects via an Object-Centric Layered RepresentationCode1
Segmentation Guided Deep HDR Deghosting—0
Self-Supervised Deep Subspace Clustering with Entropy-norm—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