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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 26–50 of 85 papers

TitleStatusHype
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature EmbeddingCode0
Learning Intrinsic Image Decomposition from Watching the WorldCode0
Unified Depth Prediction and Intrinsic Image Decomposition from a Single Image via Joint Convolutional Neural FieldsCode0
Single Image Intrinsic Decomposition without a Single Intrinsic Image—0
Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition—0
Single Image Layer Separation using Relative Smoothness—0
Intrinsic Decomposition of Image Sequences From Local Temporal Variations—0
Intrinsic Image Decomposition for Robust Self-supervised Monocular Depth Estimation on Reflective Surfaces—0
Intrinsic Image Decomposition using Paradigms—0
3D Face Mask Presentation Attack Detection Based on Intrinsic Image Analysis—0
Intrinsic Image Transformation via Scale Space Decomposition—0
Intrinsic Scene Decomposition From RGB-D images—0
Invariant Descriptors for Intrinsic Reflectance Optimization—0
JoIN: Joint GANs Inversion for Intrinsic Image Decomposition—0
Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images—0
Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Faces—0
Learning Data-driven Reflectance Priors for Intrinsic Image Decomposition—0
Learning Intrinsic Images for Clothing—0
Learning Lightness From Human Judgement on Relative Reflectance—0
Learning Ordinal Relationships for Mid-Level Vision—0
Learning to Factorize and Relight a City—0
Leveraging Multi-view Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation—0
Light Source Separation and Intrinsic Image Decomposition Under AC Illumination—0
Measured Albedo in the Wild: Filling the Gap in Intrinsics Evaluation—0
Non-Local Intrinsic Decomposition With Near-Infrared Priors—0
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