https://github.com/ljw-git/arbitrary_resolution_rppg
공식 코드Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videos
Papers with Code 아카이브
Optical Flow Estimation
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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/lliuz/ARFlow
공식 코드https://github.com/lmb-freiburg/sf2se3
공식 코드SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection
https://github.com/lmb-freiburg/understanding_flow_robustness
공식 코드Towards Understanding Adversarial Robustness of Optical Flow Networks
https://github.com/locuslab/deq-flow
공식 코드https://github.com/lppllppl920/DenseDescriptorLearning-Pytorch
공식 코드Extremely Dense Point Correspondences using a Learned Feature Descriptor
https://github.com/ltkong218/FastFlowNet
공식 코드FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation
https://github.com/ltkong218/ifrnet
공식 코드IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation
https://github.com/ltkong218/mdflow
공식 코드MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation
https://github.com/ltkong218/safnet
공식 코드SAFNet: Selective Alignment Fusion Network for Efficient HDR Imaging
https://github.com/ltkong218/waveletvfi
공식 코드https://github.com/lufficc/sc-transformer
공식 코드SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection
https://github.com/luka0714/VFIDKR
공식 코드https://github.com/luxiangju-personai/clothformer
공식 코드https://github.com/luyukang/DCTNet
공식 코드Depth-Cooperated Trimodal Network for Video Salient Object Detection
https://github.com/lxtGH/SFSegNets
공식 코드https://github.com/ly-zhu/Leveraging-Category-Information-for-Single-Frame-Visual-Sound-Source-Separation
공식 코드Leveraging Category Information for Single-Frame Visual Sound Source Separation
https://github.com/mallikagarg/gestformer
공식 코드GestFormer: Multiscale Wavelet Pooling Transformer Network for Dynamic Hand Gesture Recognition
https://github.com/mapolinario94/s-tllr
공식 코드S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks
https://github.com/martinetoering/ViCC
공식 코드Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting
https://github.com/masterhow/csflow
공식 코드CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous Driving
https://github.com/masterhow/flowlens
공식 코드Beyond the Field-of-View: Enhancing Scene Visibility and Perception with Clip-Recurrent Transformer
https://github.com/masterhow/panoflow
공식 코드PanoFlow: Learning 360° Optical Flow for Surrounding Temporal Understanding
https://github.com/mattpoggi/depthstillation
공식 코드https://github.com/mattpoggi/sensor-guided-flow
공식 코드https://github.com/mcahny/Deep-Video-Inpainting
공식 코드https://github.com/mcg-nju/camliflow
공식 코드CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow Estimation
https://github.com/mcg-nju/mgmae
공식 코드https://github.com/mcgridles/LENS
공식 코드https://github.com/megvii-research/GyroFlow
공식 코드GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning
https://github.com/megvii-research/realflow
공식 코드RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos
https://github.com/melgharib/DSBD
공식 코드https://github.com/mengyou2/decoulpingnerf
공식 코드Decoupling Dynamic Monocular Videos for Dynamic View Synthesis
https://github.com/mengyuest/SIGNet
공식 코드SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception
https://github.com/meyerls/fruitnerf
공식 코드FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework
https://github.com/mezzelfo/MotionCraft
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.