Joint Material and Illumination Estimation from Photo Sets in the Wild

Faithful manipulation of shape, material, and illumination in 2D Internet images would greatly benefit from a reliable factorization of appearance into material (i.e., diffuse and specular) and illumination (i.e., environment maps). On the one hand, current methods that produce very high fidelity results, typically require controlled settings, expensive devices, or significant manual effort. To the other hand, methods that are automatic and work on 'in the wild' Internet images, often extract only low-frequency lighting or diffuse materials. In this work, we propose to make use of a set of photographs in order to jointly estimate the non-diffuse materials and sharp lighting in an uncontrolled setting. Our key observation is that seeing multiple instances of the same material under different illumination (i.e., environment), and different materials under the same illumination provide valuable constraints that can be exploited to yield a high-quality solution (i.e., specular materials and environment illumination) for all the observed materials and environments. Similar constraints also arise when observing multiple materials in a single environment, or a single material across multiple environments. The core of this approach is an optimization procedure that uses two neural networks that are trained on synthetic images to predict good gradients in parametric space given observation of reflected light. We evaluate our method on a range of synthetic and real examples to generate high-quality estimates, qualitatively compare our results against state-of-the-art alternatives via a user study, and demonstrate photo-consistent image manipulation that is otherwise very challenging to achieve

A Neural Height-Map Approach for the Binocular Photometric Stereo Problem

In this work we propose a novel, highly practical, binocular photometric stereo (PS) framework, which has same acquisition speed as single view PS, however significantly improves the quality of the estimated geometry. As in recent neural multi-view shape estimation frameworks such as NeRF, SIREN and inverse graphics approaches to multi-view photometric stereo (e.g. PS-NeRF) we formulate shape estimation task as learning of a differentiable surface and texture representation by minimising surface normal discrepancy for normals estimated from multiple varying light images for two views as well as discrepancy between rendered surface intensity and observed images. Our method differs from typical multi-view shape estimation approaches in two key ways. First, our surface is represented not as a volume but as a neural heightmap where heights of points on a surface are computed by a deep neural network. Second, instead of predicting an average intensity as PS-NeRF or introducing lambertian material assumptions as Guo et al., we use a learnt BRDF and perform near-field per point intensity rendering. Our method achieves the state-of-the-art performance on the DiLiGenT-MV dataset adapted to binocular stereo setup as well as a new binocular photometric stereo dataset - LUCES-ST.Comment: WACV 202

Epälambertilaiset pinnat ja niiden haasteet konenäössä

This thesis regards non-Lambertian surfaces and their challenges, solutions and study in computer vision. The physical theory for understanding the phenomenon is built first, using the Lambertian reflectance model, which defines Lambertian surfaces as ideally diffuse surfaces, whose luminance is isotropic and the luminous intensity obeys Lambert's cosine law. From these two assumptions, non-Lambertian surfaces violate at least the cosine law and are consequently specularly reflecting surfaces, whose perceived brightness is dependent from the viewpoint. Thus non-Lambertian surfaces violate also brightness and colour constancies, which assume that the brightness and colour of same real-world points stays constant across images. These assumptions are used, for example, in tracking and feature matching and thus non-Lambertian surfaces pose complications for object reconstruction and navigation among other tasks in the field of computer vision. After formulating the theoretical foundation of necessary physics and a more general reflectance model called the bi-directional reflectance distribution function, a comprehensive literature review into significant studies regarding non-Lambertian surfaces is conducted. The primary topics of the survey include photometric stereo and navigation systems, while considering other potential fields, such as fusion methods and illumination invariance. The goal of the survey is to formulate a detailed and in-depth answer to what methods can be used to solve the challenges posed by non-Lambertian surfaces, what are these methods' strengths and weaknesses, what are the used datasets and what remains to be answered by further research. After the survey, a dataset is collected and presented, and an outline of another dataset to be published in an upcoming paper is presented. Then a general discussion about the survey and the study is undertaken and conclusions along with proposed future steps are introduced

Scene relighting and editing for improved object insertion

Abstract. The goal of this thesis is to develop a scene relighting and object insertion pipeline using Neural Radiance Fields (NeRF) to incorporate one or more objects into an outdoor environment scene. The output is a 3D mesh that embodies decomposed bidirectional reflectance distribution function (BRDF) characteristics, which interact with varying light source positions and strengths. To achieve this objective, the thesis is divided into two sub-tasks. The first sub-task involves extracting visual information about the outdoor environment from a sparse set of corresponding images. A neural representation is constructed, providing a comprehensive understanding of the constituent elements, such as materials, geometry, illumination, and shadows. The second sub-task involves generating a neural representation of the inserted object using either real-world images or synthetic data. To accomplish these objectives, the thesis draws on existing literature in computer vision and computer graphics. Different approaches are assessed to identify their advantages and disadvantages, with detailed descriptions of the chosen techniques provided, highlighting their functioning to produce the ultimate outcome. Overall, this thesis aims to provide a framework for compositing and relighting that is grounded in NeRF and allows for the seamless integration of objects into outdoor environments. The outcome of this work has potential applications in various domains, such as visual effects, gaming, and virtual reality