Shape Recovery refers to the process of extracting three-dimensional (3D) information about the scene or objects captured by a light field camera. Light-field cameras are designed to capture not only the intensity of light but also the direction in which light rays are traveling. This additional information allows for post-capture processing that can be used to infer the depth or shape of objects within the scene. Shape recovery in the context of light-field cameras extends the capabilities of traditional photography by enabling the capture and analysis of 3D information, offering new possibilities for image manipulation, computer vision, and immersive media experiences.

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SVBRDF-Invariant Shape and Reflectance Estimation from a Light-Field Camera

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Light-field cameras have recently emerged as a powerful tool for one-shot passive 3D shape capture. However, obtaining the shape of glossy objects like metals or plastics remains challenging, since standard Lambertian cues like photo-consistency cannot be easily applied. In this paper, we derive a spatially-varying (SV)BRDF-invariant theory for recovering 3D shape and reflectance from light-field cameras. Our key theoretical insight is a novel analysis of diffuse plus single-lobe SVBRDFs under a light-field setup. We show that, although direct shape recovery is not possible, an equation relating depths and normals can still be derived. Using this equation, we then propose using a polynomial (quadratic) shape prior to resolve the shape ambiguity. Once shape is estimated, we also recover the reflectance. We present extensive synthetic data on the entire MERL BRDF dataset, as well as a number of real examples to validate the theory, where we simultaneously recover shape and BRDFs from a single image taken with a Lytro Illum camera.

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