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Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach

2019-12-13 14:07:30
Lu Sang, Bjoern Haefner, Daniel Cremers
arXiv_CV
       
Abstract

A novel approach towards depth map super-resolution using multi-view uncalibrated photometric stereo is presented. Practically, an LED light source is attached to a commodity RGB-D sensor and is used to capture objects from multiple viewpoints with unknown motion. This non-static camera-to-object setup is described with a nonconvex variational approach such that no calibration on lighting or camera motion is required due to the formulation of an end-to-end joint optimization problem. Solving the proposed variational model results in high resolution depth, reflectance and camera pose estimates, as we show on challenging synthetic and real-world datasets.

Abstract (translated)

URL

https://arxiv.org/abs/1912.06501

PDF

https://arxiv.org/pdf/1912.06501.pdf

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