Motion Segmentation
31 papers with code • 4 benchmarks • 4 datasets
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.
Benchmarks
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Hopkins155
EV-IMO
KT3DMoSeg
Greatest papers with code
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions.
Ranked #30 on
Monocular Depth Estimation
on KITTI Eigen split
FlowNet3D: Learning Scene Flow in 3D Point Clouds
xingyul/flownet3d
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CVPR 2019
In this work, we propose a novel deep neural network named $FlowNet3D$ that learns scene flow from point clouds in an end-to-end fashion.
Sparse Subspace Clustering: Algorithm, Theory, and Applications
panji530/Deep-subspace-clustering-networks
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In this paper, we propose and study an algorithm, called Sparse Subspace Clustering (SSC), to cluster data points that lie in a union of low-dimensional subspaces.
Ranked #4 on
Motion Segmentation
on Hopkins155
UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos
baidu-research/UnDepthflow
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CVPR 2019
In this paper, we propose UnOS, an unified system for unsupervised optical flow and stereo depth estimation using convolutional neural network (CNN) by taking advantages of their inherent geometrical consistency based on the rigid-scene assumption.
Learning to Segment Rigid Motions from Two Frames
gengshan-y/rigidmask
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CVPR 2021
Geometric motion segmentation algorithms, however, generalize to novel scenes, but have yet to achieve comparable performance to appearance-based ones, due to noisy motion estimations and degenerate motion configurations.
Distilled Semantics for Comprehensive Scene Understanding from Videos
CVLAB-Unibo/omeganet
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CVPR 2020
Whole understanding of the surroundings is paramount to autonomous systems.
SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation
SST extracts per-pixel representations for each object in a video using sparse attention over spatiotemporal features.
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization
huangjh-pub/multibody-sync
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CVPR 2021
We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds.
Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm
The Progressive-X algorithm, Prog-X in short, is proposed for geometric multi-model fitting.
Motion-based Object Segmentation based on Dense RGB-D Scene Flow
stanford-iprl-lab/sceneflownet
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Our model jointly estimates (i) the segmentation of the scene into an unknown but finite number of objects, (ii) the motion trajectories of these objects and (iii) the object scene flow.
