https://github.com/facebookresearch/fast3r
공식 코드Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
Papers with Code 아카이브
3D Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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3D Reconstruction is the task of creating a 3D model or representation of an object or scene from 2D images or other data sources. The goal of 3D reconstruction is to create a virtual representation of an object or scene that can be used for a variety of purposes, such as visualization, animation, simulation, and analysis. It can be used in fields such as computer vision, robotics, and virtual reality.
Image: Gwak et al
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/facebookresearch/lightplane
공식 코드https://github.com/facebookresearch/mcc
공식 코드https://github.com/facebookresearch/replay_dataset
공식 코드Replay: Multi-modal Multi-view Acted Videos for Casual Holography
https://github.com/facebookresearch/silk
공식 코드https://github.com/facebookresearch/vggt
공식 코드https://github.com/fan-aaron-zhang/bvi-cr
공식 코드BVI-CR: A Multi-View Human Dataset for Volumetric Video Compression
https://github.com/fanhqme/PointSetGeneration
공식 코드A Point Set Generation Network for 3D Object Reconstruction from a Single Image
https://github.com/faniamokhayeri/DSFS
공식 코드Domain-Specific Face Synthesis for Video Face Recognition from a Single Sample Per Person
https://github.com/faridyagubbayli/LegoFormer
공식 코드LegoFormer: Transformers for Block-by-Block Multi-view 3D Reconstruction
https://github.com/faysal-md/unmasking-deepfake-faces-from-videos-an-explainable-cost-sensitive-deep-learning-approach-ieee2023
공식 코드Unmasking Deepfake Faces from Videos Using An Explainable Cost-Sensitive Deep Learning Approach
https://github.com/fdarmon/neuralwarp
공식 코드Improving neural implicit surfaces geometry with patch warping
https://github.com/fead1/uhrnet
공식 코드UHRNet: A Deep Learning-Based Method for Accurate 3D Reconstruction from a Single Fringe-Pattern
https://github.com/fedeloper/binary_deepfake_detection
공식 코드Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks
https://github.com/fei960922/GPointNet
공식 코드https://github.com/feixh/GeoSup
공식 코드https://github.com/filby89/spectre
공식 코드Visual Speech-Aware Perceptual 3D Facial Expression Reconstruction from Videos
https://github.com/fisseha21/z-upscaling
공식 코드Z-upscaling: Optical Flow Guided Frame Interpolation for Isotropic Reconstruction of 3D EM Volumes
https://github.com/fomalhautb/3d-retr
공식 코드3D-RETR: End-to-End Single and Multi-View 3D Reconstruction with Transformers
https://github.com/fraunhoferhhi/neural-deferred-shading
공식 코드https://github.com/fraunhoferhhi/soloplanes
공식 코드Multi-task Planar Reconstruction with Feature Warping Guidance
https://github.com/fudan-zvg/openocc
공식 코드OpenOcc: Open Vocabulary 3D Scene Reconstruction via Occupancy Representation
https://github.com/fuenwang/LED2-Net
공식 코드LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering
https://github.com/ganlab/ostra
공식 코드A One Stop 3D Target Reconstruction and multilevel Segmentation Method
https://github.com/ganwanshui/simpleoccupancy
공식 코드A Simple Framework for 3D Occupancy Estimation in Autonomous Driving
https://github.com/gap-lab-cuhk-sz/rec-mv
공식 코드REC-MV: REconstructing 3D Dynamic Cloth from Monocular Videos
https://github.com/garyzhu1996/umiformer
공식 코드UMIFormer: Mining the Correlations between Similar Tokens for Multi-View 3D Reconstruction
https://github.com/gcchen97/ppa4p3d
공식 코드Perspective Phase Angle Model for Polarimetric 3D Reconstruction
https://github.com/gen-verse/widerange4d
공식 코드WideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes
https://github.com/gengshan-y/rac
공식 코드https://github.com/geopavlakos/multishot
공식 코드https://github.com/georg-bn/rotation-steerers
공식 코드Steerers: A framework for rotation equivariant keypoint descriptors
https://github.com/geyouzhang/bsc
공식 코드Binomial Self-compensation for Motion Error in Dynamic 3D Scanning
https://github.com/ghl1995/denoise-gan-in-cryo-EM
공식 코드Generative Adversarial Networks for Robust Cryo-EM Image Denoising
https://github.com/ghuawhu/end-to-end-synthetic-speech-detection
공식 코드https://github.com/gilikn/fakeout
공식 코드FakeOut: Leveraging Out-of-domain Self-supervision for Multi-modal Video Deepfake Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.