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Title:Simple image depth and BRDF estimation
Author(s):Jeong, Dohun
Contributor(s):Do, Minh
Subject(s):Differentiable Rendering
Variational Inference
Computer Vision
Depth Estimation
Abstract:Classical computer vision algorithms for scene reconstructions have restrictive assumptions about the scene, such as the absence of specular reflection and indirect lighting. However, in most real world photographs, these assumptions often fail. Recent advancement in physically based rendering and neural networks is changing how we can infer scene parameters from a single photograph. Solving the rendering equation to simulate the light transport allows us to synthesize photorealistic images from physical scene parameters. These images take indirect lighting, specular reflections, and other phenomena into account. Differentiable rendering incorporates auto-differentiation and backpropagation to optimize the physical scene parameters based on the derivatives of the rendering equation with respect to the scene parameters. Free from the restrictions of classical computer vision algorithms, differentiable rendering can be used to reconstruct a 3D scene, and perform relighting, material editing, and other applications. This thesis investigates the recovery of intrinsic scene parameters through differentiable rendering and variational inference. The bi-directional reflectance function (BRDF) of materials are estimated using a flash-no flash image pair and scene geometry input. With the ubiquity of depth cameras and time of flight sensors in new hardware, as well as 3D indoor geometry reconstruction algorithms, this thesis assumes known scene geometry. Depth is estimated through variational inference of the collapsed dimension, by finding a hidden pattern in the latent space. This closely follows a recent study on visual deprojection to recover a collapsed dimension of an image. Combined, estimated BRDF and depth can decompose images in Intrinsic Image Decomposition, and derive normal maps, albedo estimation, and beyond.
Issue Date:2020-05
Date Available in IDEALS:2020-06-12

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