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Intrinsic Image Decomposition

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

Papers

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Showing 51–75 of 85 papers

TitleStatusHype
Objective, Absolute and Hue-aware Metrics for Intrinsic Image Decomposition on Real-World Scenes: A Proof of Concept—0
Single-image Reflectance and Transmittance Estimation from Any Flatbed Scanner—0
The Photometry of Intrinsic Images—0
Physics-based Shading Reconstruction for Intrinsic Image Decomposition—0
Towards Geometry Guided Neural Relighting with Flash Photography—0
PRISM: A Unified Framework for Photorealistic Reconstruction and Intrinsic Scene Modeling—0
SAIL: Self-supervised Albedo Estimation from Real Images with a Latent Diffusion Model—0
Self-Supervised Intrinsic Image Decomposition—0
Self-Supervised Intrinsic Image Decomposition Network Considering Reflectance Consistency—0
Semantic Hierarchical Priors for Intrinsic Image Decomposition—0
Shading Annotations in the Wild—0
An Optical physics inspired CNN approach for intrinsic image decomposition—0
A Novel Intrinsic Image Decomposition Method to Recover Albedo for Aerial Images in Photogrammetry Processing—0
A Survey on Intrinsic Images: Delving Deep Into Lambert and Beyond—0
A Theory of Joint Light and Heat Transport for Lambertian Scenes—0
CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering—0
CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition—0
A deep learning based interactive sketching system for fashion images design—0
Color naming guided intrinsic image decomposition—0
Complementary Intrinsics From Neural Radiance Fields and CNNs for Outdoor Scene Relighting—0
Consistency-aware Shading Orders Selective Fusion for Intrinsic Image Decomposition—0
Constrained Structured Regression with Convolutional Neural Networks—0
DARN: a Deep Adversial Residual Network for Intrinsic Image Decomposition—0
Deep Hybrid Real and Synthetic Training for Intrinsic Decomposition—0
Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression—0
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