Papers with Code 아카이브
3D Reconstruction
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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/Seven-year-promise/LSD_EBM_MRI
공식 코드https://github.com/Shiaoming/ALIKED
공식 코드ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation
https://github.com/ShivamDuggal4/TARS3D
공식 코드Topologically-Aware Deformation Fields for Single-View 3D Reconstruction
https://github.com/ShnitzelKiller/Reverse-Engineering-Carpentry
공식 코드https://github.com/ShunChengWu/SCFusion/blob/main/README.md
공식 코드SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion
https://github.com/SibylGao/MSCVP-MVSNet
공식 코드Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view Stereo
https://github.com/SkyworkAI/Gamba
공식 코드Gamba: Marry Gaussian Splatting with Mamba for single view 3D reconstruction
https://github.com/SparklingH/BloomScene
공식 코드BloomScene: Lightweight Structured 3D Gaussian Splatting for Crossmodal Scene Generation
https://github.com/StarRealMan/EINRUL
공식 코드https://github.com/Svito-zar/NN-for-Missing-Marker-Reconstruction
공식 코드A Neural Network Approach to Missing Marker Reconstruction in Human Motion Capture
https://github.com/TUTvision/Online-Panoptic-3D
공식 코드Online panoptic 3D reconstruction as a Linear Assignment Problem
https://github.com/Taekbum/category-nerf-reconstruction-official
공식 코드Category-level Neural Field for Reconstruction of Partially Observed Objects in Indoor Environment
https://github.com/TaoHuUMD/3D-Reconstruction
공식 코드Learning to Generate Dense Point Clouds with Textures on Multiple Categories
https://github.com/Tech4DLab/CalibMarker
공식 코드Marker-Based Extrinsic Calibration Method for Accurate Multi-Camera 3D Reconstruction
https://github.com/TencentYoutuResearch/OpenWorld-DeepFakeAttribution
공식 코드Contrastive Pseudo Learning for Open-World DeepFake Attribution
https://github.com/VAST-AI-Research/TripoSG
공식 코드TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models
https://github.com/VCL3D/DeepDepthDenoising
공식 코드https://github.com/VITA-Group/NeuralLift-360
공식 코드NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360° Views
https://github.com/VITA-Group/VLM-3R
공식 코드VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction
https://github.com/WU-CVGL/SIU3R
공식 코드SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment
https://github.com/Woo-Seok-Kim/ref-square-NeRF
공식 코드REF$^2$-NeRF: Reflection and Refraction aware Neural Radiance Field
https://github.com/Xharlie/DISN
공식 코드DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
https://github.com/Xharlie/ShapenetRender_more_variation
공식 코드DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
https://github.com/Xharlie/pointnerf
공식 코드https://github.com/Xiaorui-Huang/DISORF
공식 코드DISORF: A Distributed Online 3D Reconstruction Framework for Mobile Robots
https://github.com/YZY-stack/DF40
공식 코드https://github.com/YabinXuTUD/HRBFFusion3D
공식 코드HRBF-Fusion: Accurate 3D reconstruction from RGB-D data using on-the-fly implicits
https://github.com/Yanbo-23/OGGSplat
공식 코드OGGSplat: Open Gaussian Growing for Generalizable Reconstruction with Expanded Field-of-View
https://github.com/Yang7879/AttSets
공식 코드Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction
https://github.com/YoYo000/BlendedMVS
공식 코드BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks
https://github.com/YueJiang-nj/CVPR2020-SDFDiff
공식 코드SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization
https://github.com/Yuto-Matsunaga/Prompt_Tuning_for_Audio_Deepfake_Detection
공식 코드https://github.com/ZEISS/iPad_TrueDepth_Issue_App
공식 코드On the Issues of TrueDepth Sensor Data for Computer Vision Tasks Across Different iPad Generations
https://github.com/ZEISS/iPad_TrueDepth_Issue_Eval
공식 코드On the Issues of TrueDepth Sensor Data for Computer Vision Tasks Across Different iPad Generations
https://github.com/a-nau/synthetic-dataset-generation
공식 코드Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics
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