Papers with Code 아카이브
Motion Segmentation
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/toytiny/cmflow
공식 코드Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision
https://github.com/toytiny/raflow
공식 코드Self-Supervised Scene Flow Estimation with 4-D Automotive Radar
https://github.com/tub-rip/event_based_image_rec_inverse_problem
공식 코드https://github.com/valeoai/WoodScape/tree/master/omnidet
공식 코드OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving
https://github.com/vlar-group/freegave
공식 코드FreeGave: 3D Physics Learning from Dynamic Videos by Gaussian Velocity
https://github.com/xingyul/flownet3d
공식 코드https://github.com/yihongsun/mod-uv
공식 코드MOD-UV: Learning Mobile Object Detectors from Unlabeled Videos
https://github.com/zpbao/discovery_obj_move
공식 코드https://github.com/FilippoAleotti/Dwarf-Tensorflow
https://github.com/IIT-PAVIS/subspace-clustering-action-recognition
Sparse Subspace Clustering: Algorithm, Theory, and Applications
https://github.com/MSiam/video_class_agnostic_segmentation
Video Class Agnostic Segmentation Benchmark for Autonomous Driving
https://github.com/achalddave/segment-any-moving
https://github.com/danini/affine-correspondences-for-camera-geometry
Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm
https://github.com/danini/progressive-x
Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm
https://github.com/hyangwinter/flownet3d_pytorch
https://github.com/iremarpag/Comp423
https://github.com/iremarpag/Computer-Vision-for-Autonomous-Driving
https://github.com/ma8sa/Undersrtanding-Dynamic-Scenes-using-MR-GCN
Understanding Dynamic Scenes using Graph Convolution Networks
https://github.com/matheuscnali/monocular_visual_odometry
Moving Objects Detection with a Moving Camera: A Comprehensive Review
https://github.com/matheuscnali/monocular_vo
Moving Objects Detection with a Moving Camera: A Comprehensive Review
https://github.com/mfaisal59/EpipolarScore
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency
https://github.com/mfaisal59/RBSF
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency
https://github.com/multimodallearning/flownet3d.pytorch
https://github.com/panji1990/Deep-subspace-clustering-networks
Sparse Subspace Clustering: Algorithm, Theory, and Applications
https://github.com/panji530/Deep-subspace-clustering-networks
Sparse Subspace Clustering: Algorithm, Theory, and Applications
https://github.com/rabbityl/DeformingThings4D
https://github.com/remindof/EV-MotionSeg
https://github.com/sohanghosh29/Clustering-Codes
Sparse Subspace Clustering: Algorithm, Theory, and Applications
https://github.com/stanford-iprl-lab/sceneflownet
Motion-based Object Segmentation based on Dense RGB-D Scene Flow
https://github.com/valeoai/FLOT
https://github.com/vinits5/learning3d
https://github.com/wecacuee/articulated-slam
https://github.com/xingyul/cpnet
https://github.com/xingyul/meteornet
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