https://github.com/amyxlase/relpose-plus-plus
공식 코드RelPose++: Recovering 6D Poses from Sparse-view Observations
Papers with Code 아카이브
3D Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 10
- 보존된 평가 기록
- 44
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/anciukevicius/renderdiffusion
공식 코드RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation
https://github.com/andreaconti/multi-view-guided-multi-view-stereo
공식 코드https://github.com/andreaconti/ramdepth
공식 코드Range-Agnostic Multi-View Depth Estimation With Keyframe Selection
https://github.com/andrekassis/ai-watermark
공식 코드UnMarker: A Universal Attack on Defensive Image Watermarking
https://github.com/andyzeng/3dmatch-toolbox
공식 코드3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
https://github.com/angeladai/sgnn
공식 코드SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scans
https://github.com/angeladai/spsg
공식 코드SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans
https://github.com/anger-man/unsupervised-depth-estimation
공식 코드Unsupervised Single-shot Depth Estimation using Perceptual Reconstruction
https://github.com/anitarau/simcol
공식 코드https://github.com/anmol2059/svdd2024
공식 코드https://github.com/anonrep/iba-for-video-sequences
공식 코드Extending Information Bottleneck Attribution to Video Sequences
https://github.com/anonymous-researcher-01/physicsnerf
공식 코드PhysicsNeRF: Physics-Guided 3D Reconstruction from Sparse Views
https://github.com/anonymoususera/ava
공식 코드AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection
https://github.com/anubhav1997/youneednodataset
공식 코드A Dataless FaceSwap Detection Approach Using Synthetic Images
https://github.com/anything-of-anything/anything-3d
공식 코드Anything-3D: Towards Single-view Anything Reconstruction in the Wild
https://github.com/apple/ml-finerecon
공식 코드FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction
https://github.com/apple/ml-live-pose
공식 코드LivePose: Online 3D Reconstruction from Monocular Video with Dynamic Camera Poses
https://github.com/arclab-mit/space-nvs
공식 코드DreamSat: Towards a General 3D Model for Novel View Synthesis of Space Objects
https://github.com/arlo0o/htcl
공식 코드Hierarchical Temporal Context Learning for Camera-based Semantic Scene Completion
https://github.com/arminmasoumian/gcndepth
공식 코드GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network
https://github.com/asafkar/deep_shadow
공식 코드https://github.com/ashawkey/nerf2mesh
공식 코드Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement
https://github.com/astra-vision/SceneRF
공식 코드SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields
https://github.com/athaddius/stirmetrics
공식 코드Point Tracking in Surgery--The 2024 Surgical Tattoos in Infrared (STIR) Challenge
https://github.com/atr-dbi/cross3dvg
공식 코드Cross3DVG: Cross-Dataset 3D Visual Grounding on Different RGB-D Scans
https://github.com/autonomousvision/convolutional_occupancy_networks
공식 코드https://github.com/autonomousvision/differentiable_volumetric_rendering
공식 코드Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
https://github.com/autonomousvision/shape_as_points
공식 코드https://github.com/autumn999999/lusnar-dataset
공식 코드https://github.com/avinashpaliwal/coherentgs
공식 코드CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussians
https://github.com/awsaf49/sonics
공식 코드https://github.com/axelBarroso/HDD-Net
공식 코드HDD-Net: Hybrid Detector Descriptor with Mutual Interactive Learning
https://github.com/axelbarroso/scalenet
공식 코드https://github.com/bbrument/rnb-neus
공식 코드RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction
https://github.com/bean-young/ai4med
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.