https://github.com/FuxiComputerVision/Nefii
공식 코드NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illumination
Papers with Code 아카이브
Inverse Rendering
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Inverse Rendering is the task of recovering the properties of a scene, such as shape, material, and lighting, from an image or a video. The goal of inverse rendering is to determine the properties of a scene given an observation of it, and to generate new images or videos based on these properties.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Haian-Jin/TensoIR
공식 코드https://github.com/LMozart/CVPR2024-SpinUP
공식 코드Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo
https://github.com/LZleejean/TexIR_code
공식 코드Multi-view Inverse Rendering for Large-scale Real-world Indoor Scenes
https://github.com/LinjieLyu/DPI
공식 코드Diffusion Posterior Illumination for Ambiguity-aware Inverse Rendering
https://github.com/MortenHannemose/pytorch-vfi-cft
공식 코드https://github.com/NVlabs/nvdiffrast
공식 코드Modular Primitives for High-Performance Differentiable Rendering
https://github.com/NVlabs/nvdiffrec
공식 코드Extracting Triangular 3D Models, Materials, and Lighting From Images
https://github.com/NVlabs/nvdiffrecmc
공식 코드Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising
https://github.com/StanfordORB/Stanford-ORB
공식 코드Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark
https://github.com/YeeU/InverseRenderNet
공식 코드https://github.com/ZqlwMatt/PIR
공식 코드PIR: Photometric Inverse Rendering with Shading Cues Modeling and Surface Reflectance Regularization
https://github.com/agnjason/fmhr
공식 코드Fine-Grained Multi-View Hand Reconstruction Using Inverse Rendering
https://github.com/autohdr/hd-net
공식 코드https://github.com/cgtuebingen/nerd-neural-reflectance-decomposition
공식 코드NeRD: Neural Reflectance Decomposition from Image Collections
https://github.com/changwoonchoi/IBL-NeRF
공식 코드IBL-NeRF: Image-Based Lighting Formulation of Neural Radiance Fields
https://github.com/chenzhekl/iBRDF
공식 코드https://github.com/compphoto/Intrinsic
공식 코드https://github.com/darglein/ADOP
공식 코드https://github.com/darglein/NeAT
공식 코드https://github.com/ethliup/SelfDeblur
공식 코드https://github.com/freemantom/univoxel
공식 코드UniVoxel: Fast Inverse Rendering by Unified Voxelization of Scene Representation
https://github.com/fudan-zvg/tensoflow
공식 코드TensoFlow: Tensorial Flow-based Sampler for Inverse Rendering
https://github.com/gdaosu/albedo_aerial_photogrammetry
공식 코드A General Albedo Recovery Approach for Aerial Photogrammetric Images through Inverse Rendering
https://github.com/genintel/novum
공식 코드NOVUM: Neural Object Volumes for Robust Object Classification
https://github.com/google-research/tiny-differentiable-simulator
공식 코드https://github.com/google/nerfactor
공식 코드NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination
https://github.com/grignarder/high-quality-blendshape-generation
공식 코드High-Quality Mesh Blendshape Generation from Face Videos via Neural Inverse Rendering
https://github.com/guduxiaolang/gir
공식 코드GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization
https://github.com/gzhji/cali-hdr-dataset
공식 코드Virtual Home Staging: Inverse Rendering and Editing an Indoor Panorama under Natural Illumination
https://github.com/ingra14m/sire-ir
공식 코드https://github.com/ishit/nie
공식 코드A Level Set Theory for Neural Implicit Evolution under Explicit Flows
https://github.com/isl-org/objects-with-lighting
공식 코드https://github.com/jadgardner/neusky
공식 코드https://github.com/jadgardner/ns_reni
공식 코드RENI++ A Rotation-Equivariant, Scale-Invariant, Natural Illumination Prior
https://github.com/jingyangcarl/LightSamplingField
공식 코드Light Sampling Field and BRDF Representation for Physically-based Neural Rendering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.