1. Intrinsic images and shape refinement
Xinxin Zuo
02/08/2016

2. Intrinsic decomposition

3. Related works Priors local gradient (Retinex algorithm)
train classifiers
(Recovering Intrinsic Images
from a Single Image)

4. Related works
Priors
Non-local priors(A Closed-Form Solution to Retinex with Nonlocal Texture Constraints)

5. Related works Priors reflectance sparsity, L1
(Intrinsic Images Decomposition Using a Local and Global Sparse Representation of Reflectance)
(An L1 Image Transform for Edge-Preserving Smoothing and Scene- Level Intrinsic decomposition)

6. Related works
Priors
Learning (Color Constancy, Intrinsic Images, and Shape Estimation)

7. Related works Other methods Reflectance cluster , user interaction
Texture decomposition
CNN (Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression)

8. Decomposition with depth cues
Normal cues (A Simple Model for Intrinsic Image Decomposition with Depth Cues)

9. Decomposition with depth cues II
Efficient Intrinsic Image Decomposition for RGBD Images

10. Decomposition and shape refinement I
Intrinsic Scene Properties from a Single RGB-D Image CVPR13

11. Decomposition and shape refinement II
Joint estimation of depth, reflectance and illumination for depth refinement ICCV15 workshop

12. Decomposition with multiple images I
Photometric Stereo using Internet Images

13. Decomposition with multiple images II
Coherent Intrinsic Images from Photo Collections

14. Decomposition with multiple images III
Intrinsic Decomposition of Image Sequences from Local Temporal Variations

15. Decomposition with multiple images IV
Estimation of Intrinsic Image Sequences from Image+Depth Video

16. Shape refinement using shading
Single RGBD
Multi-view environment

17. Summary Intrinsic images combined with shape refinement
CNN with physics constraints