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

VISUAL TRACKING AND ILLUMINATION RECOVERY VIA SPARSE REPRESENTATION

Compressive sensing, or sparse representation, has played a fundamental role in many fields of science. It shows that the signals and images can be reconstructed from far fewer measurements than what is usually considered to be necessary. Sparsity leads to efficient estimation, efficient compression, dimensionality reduction, and efficient modeling. Recently, there has been a growing interest in compressive sensing in computer vision and it has been successfully applied to face recognition, background subtraction, object tracking and other problems. Sparsity can be achieved by solving the compressive sensing problem using L1 minimization. In this dissertation, we present the results of a study of applying sparse representation to illumination recovery, object tracking, and simultaneous tracking and recognition. Illumination recovery, also known as inverse lighting, is the problem of recovering an illumination distribution in a scene from the appearance of objects located in the scene. It is used for Augmented Reality, where the virtual objects match the existing image and cast convincing shadows on the real scene rendered with the recovered illumination. Shadows in a scene are caused by the occlusion of incoming light, and thus contain information about the lighting of the scene. Although shadows have been used in determining the 3D shape of the object that casts shadows onto the scene, few studies have focused on the illumination information provided by the shadows. In this dissertation, we recover the illumination of a scene from a single image with cast shadows given the geometry of the scene. The images with cast shadows can be quite complex and therefore cannot be well approximated by low-dimensional linear subspaces. However, in this study we show that the set of images produced by a Lambertian scene with cast shadows can be efficiently represented by a sparse set of images generated by directional light sources. We first model an image with cast shadows as composed of a diffusive part (without cast shadows) and a residual part that captures cast shadows. Then, we express the problem in an L1-regularized least squares formulation, with nonnegativity constraints (as light has to be nonnegative at any point in space). This sparse representation enjoys an effective and fast solution, thanks to recent advances in compressive sensing. In experiments on both synthetic and real data, our approach performs favorably in comparison to several previously proposed methods. Visual tracking, which consistently infers the motion of a desired target in a video sequence, has been an active and fruitful research topic in computer vision for decades. It has many practical applications such as surveillance, human computer interaction, medical imaging and so on. Many challenges to design a robust tracking algorithm come from the enormous unpredictable variations in the target, such as deformations, fast motion, occlusions, background clutter, and lighting changes. To tackle the challenges posed by tracking, we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, noise and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an L1-regularized least squares problem. Then the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. Three additional components further improve the robustness of our approach: 1) a velocity incorporated motion model that helps concentrate the samples on the true target location in the next frame, 2) the nonnegativity constraints that help filter out clutter that is similar to tracked targets in reversed intensity patterns, and 3) a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure. We test the proposed approach on many challenging sequences involving heavy occlusions, drastic illumination changes, large scale changes, non-rigid object movement, out-of-plane rotation, and large pose variations. The proposed approach shows excellent performance in comparison with four previously proposed trackers. We also extend the work to simultaneous tracking and recognition in vehicle classification in IR video sequences. We attempt to resolve the uncertainties in tracking and recognition at the same time by introducing a static template set that stores target images in various conditions such as different poses, lighting, and so on. The recognition results at each frame are propagated to produce the final result for the whole video. The tracking result is evaluated at each frame and low confidence in tracking performance initiates a new cycle of tracking and classification. We demonstrate the robustness of the proposed method on vehicle tracking and classification using outdoor IR video sequences

Surface analysis and visualization from multi-light image collections

Multi-Light Image Collections (MLICs) are stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination that provides large amounts of visual and geometric information. Over the last decades, a wide variety of methods have been devised to extract information from MLICs and have shown its use in different application domains to support daily activities. In this thesis, we present methods that leverage a MLICs for surface analysis and visualization. First, we provide background information: acquisition setup, light calibration and application areas where MLICs have been successfully used for the research of daily analysis work. Following, we discuss the use of MLIC for surface visualization and analysis and available tools used to support the analysis. Here, we discuss methods that strive to support the direct exploration of the captured MLIC, methods that generate relightable models from MLIC, non-photorealistic visualization methods that rely on MLIC, methods that estimate normal map from MLIC and we point out visualization tools used to do MLIC analysis. In chapter 3 we propose novel benchmark datasets (RealRTI, SynthRTI and SynthPS) that can be used to evaluate algorithms that rely on MLIC and discusses available benchmark for validation of photometric algorithms that can be also used to validate other MLIC-based algorithms. In chapter 4, we evaluate the performance of different photometric stereo algorithms using SynthPS for cultural heritage applications. RealRTI and SynthRTI have been used to evaluate the performance of (Neural)RTI method. Then, in chapter 5, we present a neural network-based RTI method, aka NeuralRTI, a framework for pixel-based encoding and relighting of RTI data. In this method using a simple autoencoder architecture, we show that it is possible to obtain a highly compressed representation that better preserves the original information and provides increased quality of virtual images relighted from novel directions, particularly in the case of challenging glossy materials. Finally, in chapter 6, we present a method for the detection of crack on the surface of paintings from multi-light image acquisitions and that can be used as well on single images and conclude our presentation

Scalable, Detailed and Mask-Free Universal Photometric Stereo

In this paper, we introduce SDM-UniPS, a groundbreaking Scalable, Detailed, Mask-free, and Universal Photometric Stereo network. Our approach can recover astonishingly intricate surface normal maps, rivaling the quality of 3D scanners, even when images are captured under unknown, spatially-varying lighting conditions in uncontrolled environments. We have extended previous universal photometric stereo networks to extract spatial-light features, utilizing all available information in high-resolution input images and accounting for non-local interactions among surface points. Moreover, we present a new synthetic training dataset that encompasses a diverse range of shapes, materials, and illumination scenarios found in real-world scenes. Through extensive evaluation, we demonstrate that our method not only surpasses calibrated, lighting-specific techniques on public benchmarks, but also excels with a significantly smaller number of input images even without object masks.Comment: CVPR 2023 (Highlight). The source code will be available at https://github.com/satoshi-ikehata/SDM-UniPS-CVPR202

Multiplexed Illumination for Scene Recovery in the Presence of Global Illumination

Global illumination effects such as inter-reflections and subsurface scattering result in systematic, and often significant errors in scene recovery using active illumination. Recently, it was shown that the direct and global components could be separated efficiently for a scene illuminated with a single light source. In this paper, we study the problem of direct-global separation for multiple light sources. We derive a theoretical lower bound for the number of required images, and propose a multiplexed illumination scheme which achieves this lower bound. We analyze the signal-to-noise ratio (SNR) characteristics of the proposed illumination multiplexing method in the context of direct-global separation. We apply our method to several scene recovery techniques requiring multiple light sources, including shape from shading, structured light 3D scanning, photometric stereo, and reflectance estimation. Both simulation and experimental results show that the proposed method can accurately recover scene information with fewer images compared to sequentially separating direct-global components for each light source