Motion Segmentation
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
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
FlowNet3D: Learning Scene Flow in 3D Point Clouds
CVPR 2019
• xingyul/flownet3d
• 
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
• 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.
#4 best model for
Image Clustering
on Extended Yale-B
UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos
CVPR 2019
• baidu-research/UnDepthflow
•
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.
MOTION SEGMENTATION OPTICAL FLOW ESTIMATION STEREO DEPTH ESTIMATION VISUAL ODOMETRY
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.
Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm
The Progressive-X algorithm, Prog-X in short, is proposed for geometric multi-model fitting.
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation
ECCV 2018
• FilippoAleotti/Dwarf-Tensorflow
•
Making use of the estimated occlusions, we also show improved results on motion segmentation and scene flow estimation.
MOTION SEGMENTATION OPTICAL FLOW ESTIMATION SCENE FLOW ESTIMATION
Adaptive Low-Rank Kernel Subspace Clustering
In this paper, we present a kernel subspace clustering method that can handle non-linear models.
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency
• mfaisal59/EpONet
• 
To handle the nonrigid background like a sea, we also propose a robust fusion mechanism between motion and appearance-based features.
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.