DeLight-Net: Decomposing Reflectance Maps into Specular Materials and Natural Illumination

In this paper we are extracting surface reflectance and natural environmental illumination from a reflectance map, i.e. from a single 2D image of a sphere of one material under one illumination. This is a notoriously difficult problem, yet key to various re-rendering applications. With the recent advances in estimating reflectance maps from 2D images their further decomposition has become increasingly relevant. To this end, we propose a Convolutional Neural Network (CNN) architecture to reconstruct both material parameters (i.e. Phong) as well as illumination (i.e. high-resolution spherical illumination maps), that is solely trained on synthetic data. We demonstrate that decomposition of synthetic as well as real photographs of reflectance maps, both in High Dynamic Range (HDR), and, for the first time, on Low Dynamic Range (LDR) as well. Results are compared to previous approaches quantitatively as well as qualitatively in terms of re-renderings where illumination, material, view or shape are changed.Comment: Stamatios Georgoulis and Konstantinos Rematas contributed equally to this wor

Shape recovery from reflection.

by Yingli Tian.Thesis (Ph.D.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 202-222).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Physics-Based Shape Recovery Techniques --- p.3Chapter 1.2 --- Proposed Approaches to Shape Recovery in this Thesis --- p.9Chapter 1.3 --- Thesis Outline --- p.13Chapter 2 --- Camera Model in Color Vision --- p.15Chapter 2.1 --- Introduction --- p.15Chapter 2.2 --- Spectral Linearization --- p.17Chapter 2.3 --- Image Balancing --- p.21Chapter 2.4 --- Spectral Sensitivity --- p.24Chapter 2.5 --- Color Clipping and Blooming --- p.24Chapter 3 --- Extended Light Source Models --- p.27Chapter 3.1 --- Introduction --- p.27Chapter 3.2 --- A Spherical Light Model in 2D Coordinate System --- p.30Chapter 3.2.1 --- Basic Photometric Function for Hybrid Surfaces under a Point Light Source --- p.32Chapter 3.2.2 --- Photometric Function for Hybrid Surfaces under the Spher- ical Light Source --- p.34Chapter 3.3 --- A Spherical Light Model in 3D Coordinate System --- p.36Chapter 3.3.1 --- Radiance of the Spherical Light Source --- p.36Chapter 3.3.2 --- Surface Brightness Illuminated by One Point of the Spher- ical Light Source --- p.38Chapter 3.3.3 --- Surface Brightness Illuminated by the Spherical Light Source --- p.39Chapter 3.3.4 --- Rotating the Source-Object Coordinate to the Camera- Object Coordinate --- p.41Chapter 3.3.5 --- Surface Reflection Model --- p.44Chapter 3.4 --- Rectangular Light Model in 3D Coordinate System --- p.45Chapter 3.4.1 --- Radiance of a Rectangular Light Source --- p.45Chapter 3.4.2 --- Surface Brightness Illuminated by One Point of the Rect- angular Light Source --- p.47Chapter 3.4.3 --- Surface Brightness Illuminated by a Rectangular Light Source --- p.47Chapter 4 --- Shape Recovery from Specular Reflection --- p.54Chapter 4.1 --- Introduction --- p.54Chapter 4.2 --- Theory of the First Method --- p.57Chapter 4.2.1 --- Torrance-Sparrow Reflectance Model --- p.57Chapter 4.2.2 --- Relationship Between Surface Shapes from Different Images --- p.60Chapter 4.3 --- Theory of the Second Method --- p.65Chapter 4.3.1 --- Getting the Depth of a Reference Point --- p.65Chapter 4.3.2 --- Recovering the Depth and Normal of a Specular Point Near the Reference Point --- p.67Chapter 4.3.3 --- Recovering Local Shape of the Object by Specular Reflection --- p.69Chapter 4.4 --- Experimental Results and Discussions --- p.71Chapter 4.4.1 --- Experimental System and Results of the First Method --- p.71Chapter 4.4.2 --- Experimental System and Results of the Second Method --- p.76Chapter 5 --- Shape Recovery from One Sequence of Color Images --- p.81Chapter 5.1 --- Introduction --- p.81Chapter 5.2 --- Temporal-color Space Analysis of Reflection --- p.84Chapter 5.3 --- Estimation of Illuminant Color Ks --- p.88Chapter 5.4 --- Estimation of the Color Vector of the Body-reflection Component Kl --- p.89Chapter 5.5 --- Separating Specular and Body Reflection Components and Re- covering Surface Shape and Reflectance --- p.91Chapter 5.6 --- Experiment Results and Discussions --- p.92Chapter 5.6.1 --- Results with Interreflection --- p.93Chapter 5.6.2 --- Results Without Interreflection --- p.93Chapter 5.6.3 --- Simulation Results --- p.95Chapter 5.7 --- Analysis of Various Factors on the Accuracy --- p.96Chapter 5.7.1 --- Effects of Number of Samples --- p.96Chapter 5.7.2 --- Effects of Noise --- p.99Chapter 5.7.3 --- Effects of Object Size --- p.99Chapter 5.7.4 --- Camera Optical Axis Not in Light Source Plane --- p.102Chapter 5.7.5 --- Camera Optical Axis Not Passing Through Object Center --- p.105Chapter 6 --- Shape Recovery from Two Sequences of Images --- p.107Chapter 6.1 --- Introduction --- p.107Chapter 6.2 --- Method for 3D Shape Recovery from Two Sequences of Images --- p.109Chapter 6.3 --- Genetics-Based Method --- p.111Chapter 6.4 --- Experimental Results and Discussions --- p.115Chapter 6.4.1 --- Simulation Results --- p.115Chapter 6.4.2 --- Real Experimental Results --- p.118Chapter 7 --- Shape from Shading for Non-Lambertian Surfaces --- p.120Chapter 7.1 --- Introduction --- p.120Chapter 7.2 --- Reflectance Map for Non-Lambertian Color Surfaces --- p.123Chapter 7.3 --- Recovering Non-Lambertian Surface Shape from One Color Image --- p.127Chapter 7.3.1 --- Segmenting Hybrid Areas from Diffuse Areas Using Hue Information --- p.127Chapter 7.3.2 --- Calculating Intensities of Specular and Diffuse Compo- nents on Hybrid Areas --- p.128Chapter 7.3.3 --- Recovering Shape from Shading --- p.129Chapter 7.4 --- Experimental Results and Discussions --- p.131Chapter 7.4.1 --- Simulation Results --- p.131Chapter 7.4.2 --- Real Experimental Results --- p.136Chapter 8 --- Shape from Shading under Multiple Extended Light Sources --- p.142Chapter 8.1 --- Introduction --- p.142Chapter 8.2 --- Reflectance Map for Lambertian Surface Under Multiple Rectan- gular Light Sources --- p.144Chapter 8.3 --- Recovering Surface Shape Under Multiple Rectangular Light Sources --- p.148Chapter 8.4 --- Experimental Results and Discussions --- p.150Chapter 8.4.1 --- Synthetic Image Results --- p.150Chapter 8.4.2 --- Real Image Results --- p.152Chapter 9 --- Shape from Shading in Unknown Environments by Neural Net- works --- p.167Chapter 9.1 --- Introduction --- p.167Chapter 9.2 --- Shape Estimation --- p.169Chapter 9.2.1 --- Shape Recovery Problem under Multiple Rectangular Ex- tended Light Sources --- p.169Chapter 9.2.2 --- Forward Network Representation of Surface Normals --- p.170Chapter 9.2.3 --- Shape Estimation --- p.174Chapter 9.3 --- Application of the Neural Network in Shape Recovery --- p.174Chapter 9.3.1 --- Structure of the Neural Network --- p.174Chapter 9.3.2 --- Normalization of the Input and Output Patterns --- p.175Chapter 9.4 --- Experimental Results and Discussions --- p.178Chapter 9.4.1 --- Results for Lambertian Surface under One Rectangular Light --- p.178Chapter 9.4.2 --- Results for Lambertian Surface under Four Rectangular Light Sources --- p.180Chapter 9.4.3 --- Results for Hybrid Surface under One Rectangular Light Sources --- p.190Chapter 9.4.4 --- Discussions --- p.190Chapter 10 --- Summary and Conclusions --- p.191Chapter 10.1 --- Summary Results and Contributions --- p.192Chapter 10.2 --- Directions of Future Research --- p.199Bibliography --- p.20

Depth Recovery of Complex Surfaces from Texture-less Pairs of Stereo Images

In this paper, a novel framework is presented to recover the 3D shape information of a complex surface using its texture-less stereo images. First a linear and generalized Lambertian model is proposed to obtain the depth information by shape from shading (SfS) using an image from stereo pair. Then this depth data is corrected by integrating scale invariant features (SIFT) indexes. These SIFT indexes are defined by means of disparity between the matching invariant features in rectified stereo images. The integration process is based on correcting the 3D visible surfaces obtained from SfS using these SIFT indexes. The SIFT indexes based improvement of depth values which are obtained from generalized Lambertian reflectance model is performed by a feed-forward neural network. The experiments are performed to demonstrate the usability and accuracy of the proposed framework

Tex2Shape: Detailed Full Human Body Geometry From a Single Image

We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method

Tex2Shape: Detailed Full Human Body Geometry From a Single Image

We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method