https://github.com/johnjaniczek/InfraRender
공식 코드Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model
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/jumpat/segmentanythingin3d
공식 코드https://github.com/junxuan-li/scps-nir
공식 코드Self-calibrating Photometric Stereo by Neural Inverse Rendering
https://github.com/kaist-vclab/nlos-inverse-rendering
공식 코드Self-Calibrating, Fully Differentiable NLOS Inverse Rendering
https://github.com/kaist-vclab/sparseellipsometry
공식 코드https://github.com/l1346792580123/diff
공식 코드Multiview Textured Mesh Recovery by Differentiable Rendering
https://github.com/learner-shx/svg-ir
공식 코드SVG-IR: Spatially-Varying Gaussian Splatting for Inverse Rendering
https://github.com/lei-wang-github/DeepPS2
공식 코드DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated Images
https://github.com/lez-s/materialist
공식 코드Materialist: Physically Based Editing Using Single-Image Inverse Rendering
https://github.com/ligoudaner377/homo_translucent
공식 코드Inverse Rendering of Translucent Objects using Physical and Neural Renderers
https://github.com/lmozart/cvpr2023-dani-net
공식 코드https://github.com/lwwu2/fipt
공식 코드Factorized Inverse Path Tracing for Efficient and Accurate Material-Lighting Estimation
https://github.com/lzhnb/gs-ir
공식 코드https://github.com/lzqsd/InverseRenderingOfIndoorScene
공식 코드https://github.com/maeyounes/texturesplat
공식 코드TextureSplat: Per-Primitive Texture Mapping for Reflective Gaussian Splatting
https://github.com/majita06/all-frequency_full-body_human_image_relighting
공식 코드https://github.com/megvii-research/Sobolev_INRs
공식 코드Sobolev Training for Implicit Neural Representations with Approximated Image Derivatives
https://github.com/nmwsharp/neural-implicit-queries
공식 코드Spelunking the Deep: Guaranteed Queries on General Neural Implicit Surfaces via Range Analysis
https://github.com/rando11199/triplet
공식 코드Triplet: Triangle Patchlet for Mesh-Based Inverse Rendering and Scene Parameters Approximation
https://github.com/rubenwiersma/svbrdf_uncertainty
공식 코드https://github.com/s3anwu/pbrnerf
공식 코드PBR-NeRF: Inverse Rendering with Physics-Based Neural Fields
https://github.com/shaomq2187/transpir
공식 코드Polarimetric Inverse Rendering for Transparent Shapes Reconstruction
https://github.com/skylar-sutherland/single-scan-3dmms
공식 코드https://github.com/sony/ndjir
공식 코드NDJIR: Neural Direct and Joint Inverse Rendering for Geometry, Lights, and Materials of Real Object
https://github.com/theo-wu/metaras
공식 코드https://github.com/tzofi/physically-disentangled-representations
공식 코드https://github.com/vc-bonn/rosa
공식 코드ROSA: Reconstructing Object Shape and Appearance Textures by Adaptive Detail Transfer
https://github.com/waps101/AlbedoMM
공식 코드https://github.com/wilrothman/sakira-code
공식 코드https://github.com/xk-huang/ema
공식 코드https://github.com/ycjungsubhuman/density-adaptation
공식 코드Mesh Density Adaptation for Template-based Shape Reconstruction
https://github.com/yifita/DSS
공식 코드Differentiable Surface Splatting for Point-based Geometry Processing
https://github.com/yxuhan/reflectancemm
공식 코드Learning a 3D Morphable Face Reflectance Model from Low-cost Data
https://github.com/za-cheng/wildlight
공식 코드https://github.com/zju3dv/intrinsicnerf
공식 코드IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis
https://github.com/zyp123494/dynavol
공식 코드Dynamic Scene Understanding through Object-Centric Voxelization and Neural Rendering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.