NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination

Recent advances in implicit neural representation have demonstrated the ability to recover detailed geometry and material from multi-view images. However, the use of simplified lighting models such as environment maps to represent non-distant illumination, or using a network to fit indirect light modeling without a solid basis, can lead to an undesirable decomposition between lighting and material. To address this, we propose a fully differentiable framework named neural ambient illumination (NeAI) that uses Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in a physically based way. Together with integral lobe encoding for roughness-adaptive specular lobe and leveraging the pre-convoluted background for accurate decomposition, the proposed method represents a significant step towards integrating physically based rendering into the NeRF representation. The experiments demonstrate the superior performance of novel-view rendering compared to previous works, and the capability to re-render objects under arbitrary NeRF-style environments opens up exciting possibilities for bridging the gap between virtual and real-world scenes. The project and supplementary materials are available at https://yiyuzhuang.github.io/NeAI/.Comment: Project page: <a class="link-external link-https" href="https://yiyuzhuang.github.io/NeAI/" rel="external noopener nofollow">https://yiyuzhuang.github.io/NeAI/</a

Data-Driven Reflectance Estimation Under Natural Lighting

Bidirectional Reflectance Distribution Functions, (BRDFs), describe how light is reflected off of a material. BRDFs are captured so that the materials can be re-lit under new while maintaining accuracy. BRDF models can approximate the reflectance of a material, but are unable to accurately represent the full BRDF of the material. Acquisition setups for BRDFs trade accuracy for speed with the most accurate methods, gonioreflectometers, being the slowest. Image-based BRDF acquisition approaches range from using complicated controlled lighting setups to uncontrolled known lighting to assuming the lighting is unknown. We propose a data-driven method for recovering BRDFs under known, but uncontrolled lighting. This approach utilizes a dataset of 100 measured BRDFs to accurately reconstruct the BRDF from a single photograph. We model the BRDFs as Gaussian Mixture Models, (GMMs), and use an Expectation Maximization, (EM), approach to determine cluster membership. We apply this approach to captured data as well as synthetic. We continue this work by relaxing assumptions about either lighting, material, or geometry. This work was supported in part by NSF grant IIS-1350323 and gifts from Google, Activision, and Nvidia

NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination

We address the problem of recovering the shape and spatially-varying reflectance of an object from multi-view images (and their camera poses) of an object illuminated by one unknown lighting condition. This enables the rendering of novel views of the object under arbitrary environment lighting and editing of the object's material properties. The key to our approach, which we call Neural Radiance Factorization (NeRFactor), is to distill the volumetric geometry of a Neural Radiance Field (NeRF) [Mildenhall et al. 2020] representation of the object into a surface representation and then jointly refine the geometry while solving for the spatially-varying reflectance and environment lighting. Specifically, NeRFactor recovers 3D neural fields of surface normals, light visibility, albedo, and Bidirectional Reflectance Distribution Functions (BRDFs) without any supervision, using only a re-rendering loss, simple smoothness priors, and a data-driven BRDF prior learned from real-world BRDF measurements. By explicitly modeling light visibility, NeRFactor is able to separate shadows from albedo and synthesize realistic soft or hard shadows under arbitrary lighting conditions. NeRFactor is able to recover convincing 3D models for free-viewpoint relighting in this challenging and underconstrained capture setup for both synthetic and real scenes. Qualitative and quantitative experiments show that NeRFactor outperforms classic and deep learning-based state of the art across various tasks. Our videos, code, and data are available at people.csail.mit.edu/xiuming/projects/nerfactor/.Comment: Camera-ready version for SIGGRAPH Asia 2021. Project Page: https://people.csail.mit.edu/xiuming/projects/nerfactor

SIDER: Single-Image Neural Optimization for Facial Geometric Detail Recovery

© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.We present SIDER (Single-Image neural optimization for facial geometric DEtail Recovery), a novel photometric optimization method that recovers detailed facial geometry from a single image in an unsupervised manner. Inspired by classical techniques of coarse-to-fine optimization and recent advances in implicit neural representations of 3D shape, SIDER combines a geometry prior based on statistical models and Signed Distance Functions (SDFs) to recover facial details from single images. First, it estimates a coarse geometry using a morphable model represented as an SDF. Next, it reconstructs facial geometry details by optimizing a photometric loss with respect to the ground truth image. In contrast to prior work, SIDER does not rely on any dataset priors and does not require additional supervision from multiple views, lighting changes or ground truth 3D shape. Extensive qualitative and quantitative evaluation demonstrates that our method achieves state-of-the-art on facial geometric detail recovery, using only a single in the-wild image.Peer ReviewedPostprint (author's final draft

Modelling appearance and geometry from images

Acquisition of realistic and relightable 3D models of large outdoor structures, such as buildings, requires the modelling of detailed geometry and visual appearance. Recovering these material characteristics can be very time consuming and needs specially dedicated equipment. Alternatively, surface detail can be conveyed by textures recovered from images, whose appearance is only valid under the originally photographed viewing and lighting conditions. Methods to easily capture locally detailed geometry, such as cracks in stone walls, and visual appearance require control of lighting conditions, which are usually restricted to small portions of surfaces captured at close range.This thesis investigates the acquisition of high-quality models from images, using simple photographic equipment and modest user intervention. The main focus of this investigation is on approximating detailed local depth information and visual appearance, obtained using a new image-based approach, and combining this with gross-scale 3D geometry. This is achieved by capturing these surface characteristics in small accessible regions and transferring them to the complete façade. This approach yields high-quality models, imparting the illusion of measured reflectance. In this thesis, we first present two novel algorithms for surface detail and visual appearance transfer, where these material properties are captured for small exemplars, using an image-based technique. Second, we develop an interactive solution to solve the problems of performing the transfer over both a large change in scale and to the different materials contained in a complete façade. Aiming to completely automate this process, a novel algorithm to differentiate between materials in the façade and associate them with the correct exemplars is introduced with promising results. Third, we present a new method for texture reconstruction from multiple images that optimises texture quality, by choosing the best view for every point and minimising seams. Material properties are transferred from the exemplars to the texture map, approximating reflectance and meso-structure. The combination of these techniques results in a complete working system capable of producing realistic relightable models of full building façades, containing high-resolution geometry and plausible visual appearance.EThOS - Electronic Theses Online ServiceGBUnited Kingdo