Lighting Estimation - 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.

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

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NeRF, PhySG, NeRFactor, NeRF, NRF, UNISURF

Object-based Illumination Estimation with Rendering-aware Neural Networks

Xin Wei, Guojun Chen, Yue Dong, Stephen Lin, Xin Tong

Microsoft Research Asia; Zhejiang Univiersity

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We present a scheme for fast environment light estimation from the RGBD appearance of individual objects and their local image areas. Conventional inverse rendering is too computationally demanding for real-time applications, and the performance of purely learning-based techniques may be limited by the meager input data available from individual objects. To address these issues, we propose an approach that takes advantage of physical principles from inverse rendering to constrain the solution, while also utilizing neural networks to expedite the more computationally expensive portions of its processing, to increase robustness to noisy input data as well as to improve temporal and spatial stability. This results in a rendering-aware system that estimates the local illumination distribution at an object with high accuracy and in real time. With the estimated lighting, virtual objects can be rendered in AR scenarios with shading that is consistent to the real scene, leading to improved realism.

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Scene-based lighting estimation; Object-based lighting estimation

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.

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

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nvdiffrec, NeRFactor

Inverse Path Tracing for Joint Material and Lighting Estimation

Dejan Azinovi?, Tzu-Mao Li, Anton Kaplanyan, Matthias Nie

Technical University of Munich; MIT CSAIL; Facebook Reality Labs

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Modern computer vision algorithms have brought significant advancement to 3D geometry reconstruction. However, illumination and material reconstruction remain less studied, with current approaches assuming very simplified models for materials and illumination. We introduce Inverse Path Tracing, a novel approach to jointly estimate the material properties of objects and light sources in indoor scenes by using an invertible light transport simulation. We assume a coarse geometry scan, along with corresponding images and camera poses. The key contribution of this work is an accurate and simultaneous retrieval of light sources and physically based material properties (e.g., diffuse reflectance, specular reflectance, roughness, etc.) for the purpose of editing and re-rendering the scene under new conditions. To this end, we introduce a novel optimization method using a differentiable Monte Carlo renderer that computes derivatives with respect to the estimated unknown illumination and material properties. This enables joint optimization for physically correct light transport and material models using a tailored stochastic gradient descent.

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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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.

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

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NeRF, IDR, DVR

Face relighting with radiance environment maps

Zhen Wen, Zicheng Liu, Thomas S. Huang

University of Illinois Urbana; Microsoft Research Redmond

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A radiance environment map pre-integrates a constant surface reflectance with the lighting environment. It has been used to generate photo-realistic rendering at interactive speed. However, one of its limitations is that each radiance environment map can only render the object, which has the same surface reflectance as what it integrates. We present a ratio-image based technique to use a radiance environment map to render diffuse objects with different surface reflectance properties. This method has the advantage that it does not require the separation of illumination from reflectance, and it is simple to implement and runs at interactive speed. In order to use this technique for human face relighting, we have developed a technique that uses spherical harmonics to approximate the radiance environment map for any given image of a face. Thus we are able to relight face images when the lighting environment rotates. Another benefit of the radiance environment map is that we can interactively modify lighting by changing the coefficients of the spherical harmonics basis. Finally we can modify the lighting condition of one person's face so that it matches the new lighting condition of a different person's face image assuming the two faces have similar skin albedos.

Neural Illumination: Lighting Prediction for Indoor Environments

Shuran Song, Thomas Funkhouser

Google; Princeton University

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This paper addresses the task of estimating the light arriving from all directions to a 3D point observed at a selected pixel in an RGB image. This task is challenging because it requires predicting a mapping from a partial scene observation by a camera to a complete illumination map for a selected position, which depends on the 3D location of the selection, the distribution of unobserved light sources, the occlusions caused by scene geometry, etc. Previous methods attempt to learn this complex mapping directly using a single black-box neural network, which often fails to estimate high-frequency lighting details for scenes with complicated 3D geometry. Instead, we propose "Neural Illumination" a new approach that decomposes illumination prediction into several simpler differentiable sub-tasks: 1) geometry estimation, 2) scene completion, and 3) LDR-to-HDR estimation. The advantage of this approach is that the sub-tasks are relatively easy to learn and can be trained with direct supervision, while the whole pipeline is fully differentiable and can be fine-tuned with end-to-end supervision. Experiments show that our approach performs significantly better quantitatively and qualitatively than prior work.

Shape, Illumination, and Reflectance from Shading

Jonathan T. Barron, Jitendra Malik

University of California, Berkeley

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A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world. Traditional methods for recovering scene properties such as shape, reflectance, or illumination rely on multiple observations of the same scene to overconstrain the problem. Recovering these same properties from a single image seems almost impossible in comparison -- there are an infinite number of shapes, paint, and lights that exactly reproduce a single image. However, certain explanations are more likely than others: surfaces tend to be smooth, paint tends to be uniform, and illumination tends to be natural. We therefore pose this problem as one of statistical inference, and define an optimization problem that searches for the *most likely* explanation of a single image. Our technique can be viewed as a superset of several classic computer vision problems (shape-from-shading, intrinsic images, color constancy, illumination estimation, etc) and outperforms all previous solutions to those constituent problems.

NRTF: Neural Radiance Transfer Fields for Relightable Novel-view Synthesis with Global Illumination

Error: Cannot create object

Category: Lighting Estimation

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