General Relighting - Paper Copilot

PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo

Wenqi Yang, Guanying Chen, Chaofeng Chen, Zhenfang Chen, Kwan-Yee K. Wong

The University of Hong Kong; FNii and SSE, CUHK-Shenzhen; Nanyang Technological University; MIT-IBM Watson AI Lab

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Abstract

Traditional multi-view photometric stereo (MVPS) methods are often composed of multiple disjoint stages, resulting in noticeable accumulated errors. In this paper, we present a neural inverse rendering method for MVPS based on implicit representation. Given multi-view images of a non-Lambertian object illuminated by multiple unknown directional lights, our method jointly estimates the geometry, materials, and lights. Our method first employs multi-light images to estimate per-view surface normal maps, which are used to regularize the normals derived from the neural radiance field. It then jointly optimizes the surface normals, spatially-varying BRDFs, and lights based on a shadow-aware differentiable rendering layer. After optimization, the reconstructed object can be used for novel-view rendering, relighting, and material editing. Experiments on both synthetic and real datasets demonstrate that our method achieves far more accurate shape reconstruction than existing MVPS and neural rendering methods. Our code and model can be found at this https URL.

Related Works

Single-view Photometric stereo (PS); Multi-view Photometric Stereo (MVPS); Neural Rendering

Comparisons

NeRF, PhySG, NeRFactor, NeRF, NRF, UNISURF

MII: Modeling Indirect Illumination for Inverse Rendering

Yuanqing Zhang, Jiaming Sun, Xingyi He, Huan Fu, Rongfei Jia, Xiaowei Zhou

Zhejiang University; Tao Technology Department, Alibaba Group

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Recent advances in implicit neural representations and differentiable rendering make it possible to simultaneously recover the geometry and materials of an object from multi-view RGB images captured under unknown static illumination. Despite the promising results achieved, indirect illumination is rarely modeled in previous methods, as it requires expensive recursive path tracing which makes the inverse rendering computationally intractable. In this paper, we propose a novel approach to efficiently recovering spatially-varying indirect illumination. The key insight is that indirect illumination can be conveniently derived from the neural radiance field learned from input images instead of being estimated jointly with direct illumination and materials. By properly modeling the indirect illumination and visibility of direct illumination, interreflection- and shadow-free albedo can be recovered. The experiments on both synthetic and real data demonstrate the superior performance of our approach compared to previous work and its capability to synthesize realistic renderings under novel viewpoints and illumination. Our code and data are available at https://zju3dv.github.io/invrender/.

Related Works

Inverse rendering; Implicit neural representation; Inverse rendering with implicit neural representation; The rendering equation

Comparisons

NeRFactor, PhySG

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

Xiuming Zhang, Pratul P. Srinivasan, Boyang Deng, Paul Debevec, William T. Freeman, Jonathan T. Barron

MIT CSAIL; Google Research

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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/.

Related Works

Inverse Rendering

Neural-PBIR Reconstruction of Shape, Material, and Illumination

Cheng Sun, Guangyan Cai, Zhengqin Li, Kai Yan, Cheng Zhang, Carl Marshall, Jia-Bin Huang, Shuang Zhao, Zhao Dong

Meta RLR; National Tsing Hua University; University of California, Irvine; University of Maryland, College Park

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Reconstructing the shape and spatially varying surface appearances of a physical-world object as well as its surrounding illumination based on 2D images (e.g., photographs) of the object has been a long-standing problem in computer vision and graphics. In this paper, we introduce a robust object reconstruction pipeline combining neural based object reconstruction and physics-based inverse rendering (PBIR). Specifically, our pipeline firstly leverages a neural stage to produce high-quality but potentially imperfect predictions of object shape, reflectance, and illumination. Then, in the later stage, initialized by the neural predictions, we perform PBIR to refine the initial results and obtain the final high-quality reconstruction. Experimental results demonstrate our pipeline significantly outperforms existing reconstruction methods quality-wise and performance-wise.

Related Works

Volumetric Surface Reconstruction; Material and Lighting Estimation; Physics-based Inverse Rendering

Comparisons

nvdiffrecmc, MII

Learning to Reconstruct Shape and Spatially-Varying Reflectance from a Single Image

Zhengqin Li, Zexiang Xu, Ravi Ramamoorthi, Kalyan Sunkavalli and Manmohan Chandraker

University of California, San Diego; Adobe Research

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Abstract

Reconstructing shape and reflectance properties from images is a highly under-constrained problem, and has previously been addressed by using specialized hardware to capture calibrated data or by assuming known (or highly constrained) shape or reflectance. In contrast, we demonstrate that we can recover non-Lambertian, spatially-varying BRDFs and complex geometry belonging to any arbitrary shape class, from a single RGB image captured under a combination of unknown environment illumination and flash lighting. We achieve this by training a deep neural network to regress shape and reflectance from the image. Our network is able to address this problem because of three novel contributions: first, we build a large-scale dataset of procedurally generated shapes and real-world complex SVBRDFs that approximate real world appearance well. Second, single image inverse rendering requires reasoning at multiple scales, and we propose a cascade network structure that allows this in a tractable manner. Finally, we incorporate an in-network rendering layer that aids the reconstruction task by handling global illumination effects that are important for real-world scenes. Together, these contributions allow us to tackle the entire inverse rendering problem in a holistic manner and produce state-of-the-art results on both synthetic and real data.

Related Works

Shape and Material Estimation; Deep Learning for Inverse Rendering; Rendering Layers in Deep Networks; Cascade Networks

Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising

Jon Hasselgren, Nikolai Hofmann, Jacob Munkberg

NVIDIA

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Recent advances in differentiable rendering have enabled high-quality reconstruction of 3D scenes from multi-view images. Most methods rely on simple rendering algorithms: pre-filtered direct lighting or learned representations of irradiance. We show that a more realistic shading model, incorporating ray tracing and Monte Carlo integration, substantially improves decomposition into shape, materials & lighting. Unfortunately, Monte Carlo integration provides estimates with significant noise, even at large sample counts, which makes gradient-based inverse rendering very challenging. To address this, we incorporate multiple importance sampling and denoising in a novel inverse rendering pipeline. This substantially improves convergence and enables gradient-based optimization at low sample counts. We present an efficient method to jointly reconstruct geometry (explicit triangle meshes), materials, and lighting, which substantially improves material and light separation compared to previous work. We argue that denoising can become an integral part of high quality inverse rendering pipelines.

Related Works

Neural methods for multi-view reconstruction; BRDF and lighting estimation; Image denoisers

Comparisons

nvdiffrec, NeRFactor

PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting

Kai Zhang, Fujun Luan, Qianqian Wang, Kavita Bala, Noah Snavely

Cornell University

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Abstract

We present PhySG, an end-to-end inverse rendering pipeline that includes a fully differentiable renderer and can reconstruct geometry, materials, and illumination from scratch from a set of RGB input images. Our framework represents specular BRDFs and environmental illumination using mixtures of spherical Gaussians, and represents geometry as a signed distance function parameterized as a Multi-Layer Perceptron. The use of spherical Gaussians allows us to efficiently solve for approximate light transport, and our method works on scenes with challenging non-Lambertian reflectance captured under natural, static illumination. We demonstrate, with both synthetic and real data, that our reconstructions not only enable rendering of novel viewpoints, but also physics-based appearance editing of materials and illumination.

Related Works

Neural Rendering; Material and Environment Estimation; Joint Shape and Appearance Refinement; The Rendering Equation

Comparisons

NeRF, IDR, DVR

Relighting Objects From Image Collections

Tom Haber, Christian Fuchs, Philippe Bekaer, Hans-Peter Seidel, Michael Goesele, Hendrik P. A. Lensch

Hasselt University; Ulm University; MPI Informatik; TU Darmstadt

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Abstract

We present an approach for recovering the reflectance of a static scene with known geometry from a collection of images taken under distant, unknown illumination. In contrast to previous work, we allow the illumination to vary between the images, which greatly increases the applicability of the approach. Using an all-frequency relighting framework based on wavelets, we are able to simultaneously estimate the per-image incident illumination and the persurface point reflectance. The wavelet framework allows for incorporating various reflection models. We demonstrate the quality of our results for synthetic test cases as well as for several datasets captured under laboratory conditions. Combined with multi-view stereo reconstruction, we are even able to recover the geometry and reflectance of a scene solely using images collected from the Internet.

Related Works

Reflectance from Known Illumination; Illumination Estimation; Estimating Reflectance and Illumination

Weakly-supervised Single-view Image Relighting

Renjiao Yi, Chenyang Zhu, Kai Xu

National University of Defense Technology

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Abstract

We present a learning-based approach to relight a single image of Lambertian and low-frequency specular objects. Our method enables inserting objects from photographs into new scenes and relighting them under the new environment lighting, which is essential for AR applications. To relight the object, we solve both inverse rendering and re-rendering. To resolve the ill-posed inverse rendering, we propose a weakly-supervised method by a low-rank constraint. To facilitate the weakly-supervised training, we contribute Relit, a large-scale (750K images) dataset of videos with aligned objects under changing illuminations. For re-rendering, we propose a differentiable specular rendering layer to render low-frequency non-Lambertian materials under various illuminations of spherical harmonics. The whole pipeline is end-to-end and efficient, allowing for a mobile app implementation of AR object insertion. Extensive evaluations demonstrate that our method achieves state-of-the-art performance. Project page: https://renjiaoyi.github.io/relighting/.

Related Works

Inverse Rendering; Image Relighting

MaterialGAN: Reflectance Capture using a Generative SVBRDF Model

Yu Guo, Cameron Smith, Milo

University of California, Irvine; Adobe Research

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Abstract

We address the problem of reconstructing spatially-varying BRDFs from a small set of image measurements. This is a fundamentally under-constrained problem, and previous work has relied on using various regularization priors or on capturing many images to produce plausible results. In this work, we present MaterialGAN, a deep generative convolutional network based on StyleGAN2, trained to synthesize realistic SVBRDF parameter maps. We show that MaterialGAN can be used as a powerful material prior in an inverse rendering framework: we optimize in its latent representation to generate material maps that match the appearance of the captured images when rendered. We demonstrate this framework on the task of reconstructing SVBRDFs from images captured under flash illumination using a hand-held mobile phone. Our method succeeds in producing plausible material maps that accurately reproduce the target images, and outperforms previous state-of-the-art material capture methods in evaluations on both synthetic and real data. Furthermore, our GAN-based latent space allows for high-level semantic material editing operations such as generating material variations and material morphing.

Related Works

Reflectance Capture; Generative Adversarial Networks

Category: General Relighting

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