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/gofigure-lanl/figure-segmentation
공식 코드DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding
https://github.com/google-research/nerf-from-image
공식 코드Shape, Pose, and Appearance from a Single Image via Bootstrapped Radiance Field Inversion
https://github.com/google/lasr
공식 코드LASR: Learning Articulated Shape Reconstruction from a Monocular Video
https://github.com/griegler/octnetfusion
공식 코드https://github.com/grip-unina/poi-forensics
공식 코드https://github.com/gsisaoki/omnidirectional_blender_3d_dataset
공식 코드OB3D: A New Dataset for Benchmarking Omnidirectional 3D Reconstruction Using Blender
https://github.com/guikunchen/awesome3dgs
공식 코드https://github.com/guochengqian/PU-GCN
공식 코드PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
https://github.com/gxytcrc/sgformer
공식 코드SGFormer: Satellite-Ground Fusion for 3D Semantic Scene Completion
https://github.com/gzhang8/DenseMapPosterior.jl
공식 코드A metric for evaluating 3D reconstruction and mapping performance with no ground truthing
https://github.com/haofanwang/accurate-head-pose
공식 코드https://github.com/haoz19/limr
공식 코드Learning Implicit Representation for Reconstructing Articulated Objects
https://github.com/hassony2/homan
공식 코드Towards unconstrained joint hand-object reconstruction from RGB videos
https://github.com/hebing-sjtu/S4D
공식 코드S4D: Streaming 4D Real-World Reconstruction with Gaussians and 3D Control Points
https://github.com/henry123-boy/Level-S2FM_official
공식 코드Level-S$^2$fM: Structure from Motion on Neural Level Set of Implicit Surfaces
https://github.com/henry123-boy/SpaTrackerV2
공식 코드https://github.com/hexiaoxiao-cs/dmcvr
공식 코드DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume Reconstruction
https://github.com/highwaywu/st-ddl
공식 코드Exploring Spatial-Temporal Features for Deepfake Detection and Localization
https://github.com/hirotong/reneus
공식 코드Seeing Through the Glass: Neural 3D Reconstruction of Object Inside a Transparent Container
https://github.com/hj-harry/ddip3d
공식 코드Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems
https://github.com/hk-shi/bidirectional-semisupervised-dual-branch-cnn
공식 코드https://github.com/hku-mars/mesh-learner
공식 코드https://github.com/hku-medai/endogs
공식 코드EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting
https://github.com/hku-medai/lig
공식 코드https://github.com/hkust-vgd/rfnet-4d
공식 코드https://github.com/hlzz/mirror
공식 코드Matchable Image Retrieval by Learning from Surface Reconstruction
https://github.com/hongfz16/garment4d
공식 코드Garment4D: Garment Reconstruction from Point Cloud Sequences
https://github.com/honghanh2104/t2a-think-twice-before-adaptation
공식 코드https://github.com/hongshuochen/DefakeHop
공식 코드DefakeHop: A Light-Weight High-Performance Deepfake Detector
https://github.com/hongxiaoy/ISO
공식 코드https://github.com/horizon-research/pointcloud-pipeline
공식 코드Tigris: Architecture and Algorithms for 3D Perception in Point Clouds
https://github.com/hossein-momeni/diffvox
공식 코드Differentiable Voxel-based X-ray Rendering Improves Sparse-View 3D CBCT Reconstruction
https://github.com/hsgser/self-amortized-dsw
공식 코드https://github.com/htregidgo/DissectionPhotoVolumes
공식 코드3D Reconstruction and Segmentation of Dissection Photographs for MRI-free Neuropathology
https://github.com/huangjh-pub/di-fusion
공식 코드DI-Fusion: Online Implicit 3D Reconstruction with Deep Priors
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