Papers with Code 아카이브
3D Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/huangkuns/wireframe
공식 코드Learning to Parse Wireframes in Images of Man-Made Environments
https://github.com/huawei-noah/noah-research/tree/master/CLIFF
공식 코드CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
https://github.com/hujiecpp/pe3r
공식 코드https://github.com/human-analysis/diverse3dface
공식 코드Generating Diverse 3D Reconstructions from a Single Occluded Face Image
https://github.com/hustvl/symphonies
공식 코드Symphonize 3D Semantic Scene Completion with Contextual Instance Queries
https://github.com/hygenie1228/clothwild_release
공식 코드https://github.com/hyperparams-free/hyperparams-free-3D-losses
공식 코드Hyperparameter-Free Losses for Model-Based Monocular Reconstruction
https://github.com/hzxie/Pix2Vox
공식 코드Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images
https://github.com/i2-multimedia-lab/pcc_patch
공식 코드Patch-Based Deep Autoencoder for Point Cloud Geometry Compression
https://github.com/iamwangyabin/poundnet
공식 코드https://github.com/icb-vision-ai/rslf
공식 코드Joint 3D Shape and Motion Estimation from Rolling Shutter Light-Field Images
https://github.com/icetttb/nopesac
공식 코드NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction
https://github.com/ictmcg/dna-det
공식 코드https://github.com/icvteam/ipsm
공식 코드https://github.com/idt-iti/xai-deepfakes
공식 코드Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection
https://github.com/iipl-hangzhoudianziuniversity/defn-pytorch
공식 코드https://github.com/imop-lab/defn-pytorch
공식 코드https://github.com/internlandmark/landmarksystem
공식 코드https://github.com/ishikaalunawat/231aproj
공식 코드https://github.com/ispc-lab/lidar4d
공식 코드LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis
https://github.com/issamlaradji/ssr
공식 코드SSR: Semi-supervised Soft Rasterizer for single-view 2D to 3D Reconstruction
https://github.com/ivclab/Part-Aware_Measurement_for_3D_Pose_Estimation_and_Tracking
공식 코드Part-Aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking
https://github.com/ivs3d/ivs3d
공식 코드https://github.com/jackd/template_ffd
공식 코드Learning Free-Form Deformations for 3D Object Reconstruction
https://github.com/jaegercq/4d-slingbag
공식 코드https://github.com/jarvishou829/sups
공식 코드SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous Driving
https://github.com/jasonyzhang/ners
공식 코드NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild
https://github.com/jbHu67/LoSF
공식 코드A Lightweight UDF Learning Framework for 3D Reconstruction Based on Local Shape Functions
https://github.com/jby1993/BCNet
공식 코드https://github.com/jchibane/if-net
공식 코드Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion
https://github.com/jenine-321/genface
공식 코드GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning
https://github.com/jessegator/sonar
공식 코드Where are we in audio deepfake detection? A systematic analysis over generative and detection models
https://github.com/jetd1/NeRFusion
공식 코드NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction
https://github.com/jhacsonmeza/StructuredLight_3DfreehandUS
공식 코드https://github.com/jiajiali04/3d-reconstruction-plants
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.