Progressive Acquisition of SVBRDF and Shape in Motion

Abstract

To estimate appearance parameters, traditional SVBRDF acquisition methods require multiple input images to be captured with various angles of light and camera, followed by a post‐processing step. For this reason, subjects have been limited to static scenes, or a multiview system is required to capture dynamic objects. In this paper, we propose a simultaneous acquisition method of SVBRDF and shape allowing us to capture the material appearance of deformable objects in motion using a single RGBD camera. To do so, we progressively integrate photometric samples of surfaces in motion in a volumetric data structure with a deformation graph. Then, building upon recent advances of fusion‐based methods, we estimate SVBRDF parameters in motion. We make use of a conventional RGBD camera that consists of the colour and infrared cameras with active infrared illumination. The colour camera is used for capturing diffuse properties, and the infrared camera‐illumination module is employed for estimating specular properties by means of active illumination. Our joint optimization yields complete material appearance parameters. We demonstrate the effectiveness of our method with extensive evaluation on both synthetic and real data that include various deformable objects of specular and diffuse appearance.

Deep Shape and SVBRDF Estimation using Smartphone Multi‐lens Imaging

“…Xu et al [XLZ*23] propose a method using structured light consisting of an LED array and an LCD mask coupled with a single‐lens reflex camera to obtain shape and reflectivity from a single view. Wu et al [WWZ16], Park et al [PNS18] and Ha et al [HBNK20] showed a series of progress in collecting geometry and SVBRDF, surface light field and shape in motion using RGB‐D sensors.…”