Motion Segmentation
State-of-the-art leaderboards
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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.
#17 best model for
Monocular Depth Estimation
on KITTI Eigen split
MONOCULAR DEPTH ESTIMATION MOTION ESTIMATION MOTION SEGMENTATION OPTICAL FLOW ESTIMATION
Sparse Subspace Clustering: Algorithm, Theory, and Applications
• panji1990/Deep-subspace-clustering-networks
• 
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.
SOTA for Motion Segmentation on Hopkins155
FlowNet3D: Learning Scene Flow in 3D Point Clouds
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.
Motion-based Object Segmentation based on Dense RGB-D Scene Flow
• stanford-iprl-lab/sceneflownet
•
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.
Adaptive Low-Rank Kernel Subspace Clustering
• panji1990/Low-rank-kernel-subspace-clustering
In this paper, we present a kernel subspace clustering method that can handle non-linear models.
Robust Subspace Clustering via Tighter Rank Approximation
For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method.
Extending Layered Models to 3D Motion
ECCV 2018 • donglao/layers3Dmotion
We consider the problem of inferring a layered representa-tion, its depth ordering and motion segmentation from a video in whichobjects may undergo 3D non-planar motion relative to the camera.
Robust Subspace Clustering via Smoothed Rank Approximation
However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum.