Dynamic scene shape reconstruction using a single structured light pattern

TLDR

Dynamic 3D acquisition of textured‑poor, deformable scenes is a key research area, with structured‑light methods split into temporally encoded multi‑pattern and spatially encoded single‑pattern approaches; while multi‑pattern schemes yield dense reconstructions, they struggle with rapid motion, and single‑pattern schemes, though fast, are sensitive to noise, distortions, and texture, leaving dense, stable real‑time acquisition unresolved. The authors aim to enable dense shape reconstruction from a single‑frame grid pattern image. They introduce a method that captures a single‑frame image of a grid pattern and processes it to recover dense 3D shape. The method proves robust to image‑processing challenges, achieving reliable dense reconstruction.

Abstract

3D acquisition techniques to measure dynamic scenes and deformable objects with little texture are extensively researched for applications like the motion capturing of human facial expression. To allow such measurement, several techniques using structured light have been proposed. These techniques can be largely categorized into two types. The first involves techniques to temporally encode positional information of a projector's pixels using multiple projected patterns, and the second involves techniques to spatially encode positional information into areas or color spaces. Although the former allows dense reconstruction with a sufficient number of patterns, it has difficulty in scanning objects in rapid motion. The latter technique uses only a single pattern, so this problem can be resolved, however, it often uses complex patterns or color intensities, which are weak to noise, shape distortions, or textures. Thus, it remains an open problem to achieve dense and stable 3D acquisition in real cases. In this paper, we propose a technique to achieve dense shape reconstruction that requires only a single-frame image of a grid pattern. The proposed technique also has the advantage of being robust in terms of image processing.