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Intrinsic Image Decomposition is the process of separating an image into its formation components such as reflectance (albedo) and shading (illumination). Reflectance is the color of the object, invariant to camera viewpoint and illumination conditions, whereas shading, dependent on camera viewpoint and object geometry, consists of different illumination effects, such as shadows, shading and inter-reflections. Using intrinsic images, instead of the original images, can be beneficial for many computer vision algorithms. For instance, for shape-from-shading algorithms, the shading images contain important visual cues to recover geometry, while for segmentation and detection algorithms, reflectance images can be beneficial as they are independent of confounding illumination effects. Furthermore, intrinsic images are used in a wide range of computational photography applications, such as material recoloring, relighting, retexturing and stylization.
Source: CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
MPI (Max Planck Institute) Sintel is a dataset for optical flow evaluation that has 1064 synthesized stereo images and ground truth data for disparity. Sintel is derived from open-source 3D animated short film Sintel. The dataset has 23 different scenes. The stereo images are RGB while the disparity is grayscale. Both have resolution of 1024×436 pixels and 8-bit per channel. Source: Fast Disparity Estimation using Dense Networks*
For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images. Hypersim is a photorealistic synthetic dataset for holistic indoor scene understanding. It contains 77,400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry. Source: https://github.com/apple/ml-hypersim Image Source: https://github.com/apple/ml-hypersim
We present a new large-scale photorealistic panoramic dataset named FutureHouse, which has the following characteristics. 1) It contains over 70,000 high-quality models with high-resolution meshes and physical material. All models are measured in real world standards. 2) Selected scene layouts are carefully designed by over 100 excellent artists. All of selected layouts are used in realworld display. 3) It contains 28,579 good panoramic views from 1,752 house-scale scenes. Therefore, it can be used for perspective image tasks as well as omnidirectional image tasks. 4) More physical material representation. Most materials are represent by microfacet BRDF modeling metalness, and the rest are represent by special shading models, e.g., cloth material and transmission material. 5) High rendering quality. Benefiting from commercial rendering engine, Unreal engine 4, and powerful deep learning super sampling (DLSS), our renderings have less noise. Our SVBRDF representation including base color and metalness is capable of producing nonmonochrome specular reflectance.
IBL-NeRF Dataset. Contains multi-view images with its intrinsic components.
The synthetic ShapeNet intrinsic image decomposition dataset used for training the deep CNN models IntrinsicNet and RetiNet of CVPR2018. See Section 4.1 of the paper for details.
Doc3DShade extends Doc3D with realistic lighting and shading. Follows a similar synthetic rendering procedure using captured document 3D shapes but final image generation step combines real shading of different types of paper materials under numerous illumination conditions. Source: Doc3DShade
Intrinsic component extension of MIT Multi-Illumination Dataset proposed in the paper "Intrinsic Image Decomposition via Ordinal Shading", Chris Careaga and Yağız Aksoy, ACM Transactions on Graphics, 2023 ### Project Page | Paper | Video | Supplementary | Data We provide estimations of albedo and shading for the MIT Multi-Illumination Dataset
The synthetic ShapeNet intrinsic image decomposition dataset of 90,000 images. 50,000 of them were used for training the deep CNN models of CVIU'2021 - see Section 4 of the paper. This is the extension of the first release of the synthetic ShapeNet intrinsic image decomposition dataset of 20,000 images used for training the deep CNN models IntrinsicNet and RetiNet of CVPR'2018. See Section 4.1 of the CVPR paper for the details of the data rendering.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.