Intrinsic Image Decomposition
21 papers with code • 0 benchmarks • 6 datasets
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
Benchmarks
These leaderboards are used to track progress in Intrinsic Image Decomposition
MPI Sintel
Hypersim
FutureHouse
IBL-NeRF
Doc3DShade
MID Intrinsics
Latest papers with no code
Unsupervised Intrinsic Image Decomposition with LiDAR Intensity Enhanced Training
To address this challenge, we propose a novel approach that utilizes only an image during inference while utilizing an image and LiDAR intensity during training.
Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow
In appearance adaptation, we employ the intrinsic image decomposition to embed the auxiliary daytime image and the nighttime image into a reflectance-aligned common space.
Measured Albedo in the Wild: Filling the Gap in Intrinsics Evaluation
We then finetune different algorithms on our MAW dataset to significantly improve the quality of the reconstructed albedo both quantitatively and qualitatively.
JoIN: Joint GANs Inversion for Intrinsic Image Decomposition
In this work, we propose to solve ill-posed inverse imaging problems using a bank of Generative Adversarial Networks (GAN) as a prior and apply our method to the case of Intrinsic Image Decomposition for faces and materials.
Complementary Intrinsics From Neural Radiance Fields and CNNs for Outdoor Scene Relighting
Relighting an outdoor scene is challenging due to the diverse illuminations and salient cast shadows.
Light Source Separation and Intrinsic Image Decomposition Under AC Illumination
We experimentally confirmed that our method can recover the colors of the light sources, the diffuse reflectance values, and the diffuse and specular intensities (shadings) under each of the light sources, and that the IID under AC illumination is effective for application to auto white balancing.
Seeing a Rose in Five Thousand Ways
These instances all share the same intrinsics, but appear different due to a combination of variance within these intrinsics and differences in extrinsic factors, such as pose and illumination.
Discriminative feature encoding for intrinsic image decomposition
Intrinsic image decomposition is an important and long-standing computer vision problem.
A Novel Intrinsic Image Decomposition Method to Recover Albedo for Aerial Images in Photogrammetry Processing
Recovering surface albedos from photogrammetric images for realistic rendering and synthetic environments can greatly facilitate its downstream applications in VR/AR/MR and digital twins.
Invariant Descriptors for Intrinsic Reflectance Optimization
We improve upon their model by introducing illumination invariant image descriptors: color ratios.
