https://github.com/liangp/mcstn-3dsot
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences
Papers with Code 아카이브
Optical Flow Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 10
- 보존된 평가 기록
- 133
Optical Flow Estimation is a computer vision task that involves computing the motion of objects in an image or a video sequence. The goal of optical flow estimation is to determine the movement of pixels or features in the image, which can be used for various applications such as object tracking, motion analysis, and video compression.
Approaches for optical flow estimation include correlation-based, block-matching, feature tracking, energy-based, and more recently gradient-based.
Further readings:
Definition source: Devon: Deformable Volume Network for Learning Optical Flow
Image credit: Optical Flow Estimation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lihenglin/bridge_training_code
FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning
https://github.com/lisiyao21/AnimeInterp
https://github.com/liu824/representation-flow-for-action-recognition-paddlepaddle
https://github.com/liuem607/DYAN
https://github.com/liuzhen03/ADNet
Attention-guided Network for Ghost-free High Dynamic Range Imaging
https://github.com/liuziwei7/voxel-flow
https://github.com/lmb-freiburg/demon
DeMoN: Depth and Motion Network for Learning Monocular Stereo
https://github.com/lmb-freiburg/netdef-docker
Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow
https://github.com/lmb-freiburg/optical-flow-2d-data-generation
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
https://github.com/lucidrains/perceiver-pytorch
Perceiver IO: A General Architecture for Structured Inputs & Outputs
https://github.com/lukaslang/ofcm
https://github.com/lupvasile/hd3
Hierarchical Discrete Distribution Decomposition for Match Density Estimation
https://github.com/lxx1991/pytorch-voxel-flow
https://github.com/ly-zhu/Separating-Sounds-from-a-Single-Image
Leveraging Category Information for Single-Frame Visual Sound Source Separation
https://github.com/ly-zhu/ly-zhu.github.io
Leveraging Category Information for Single-Frame Visual Sound Source Separation
https://github.com/m-serra/action-inference-for-video-prediction-benchmarking
https://github.com/ma8sa/temporal-MR-GCN
Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks
https://github.com/manastahir/Nvidia--SuperSlowMo-Keras
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
https://github.com/marcocannici/matrixlstm
A Differentiable Recurrent Surface for Asynchronous Event-Based Data
https://github.com/martkartasev/sepconv
Video Frame Interpolation via Adaptive Separable Convolution
https://github.com/masterhow/fishdreamer
Beyond the Field-of-View: Enhancing Scene Visibility and Perception with Clip-Recurrent Transformer
https://github.com/media2x/rife-ncnn-vulkan-python
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
https://github.com/megvii-research/eccv2022-rife
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
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/mgrandau/hand-tracking-stabilization
Deep Iterative Frame Interpolation for Full-frame Video Stabilization
https://github.com/microsoft/AirSim-Drone-Racing-Lab
https://github.com/microsoft/activemlp
https://github.com/mil-tokyo/FTGAN
Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture
https://github.com/mindspore-ai/models/tree/master/official/cv/pwcnet
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
https://github.com/mingyip/Motion_Compensated_FlowNet
Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion
https://github.com/msracver/Deep-Feature-Flow
https://github.com/neu-vig/ezflow
https://github.com/neuromorphicsystems/event_warping
https://github.com/ngunnar/learning-a-deformable-registration-pyramid
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.