Structured Light (SL) patterns generated based on pseudo-random arrays are widely used for single-shot 3D reconstruction using projector-camera systems. These SL images consist of a set of tags with different appearances, where these patterns will be projected on a target surface, then captured by a camera and decoded. The precision of localizing these tags from captured camera images affects the quality of the pixel-correspondences between the projector and the camera, and consequently that of the derived 3D shape. In this paper, we incorporate a quadrilateral representation for the detected SL tags that allows the construction of robust and accurate pixel-correspondences and the application of a spatial rectification module that leads to high tag classification accuracy. When applying the proposed method to single-shot 3D reconstruction, we show the effectiveness of this method over a baseline in estimating denser and more accurate 3D point-clouds.

We propose a new framework for single‐shot 3D reconstruction based on two‐dimensional tag embedding, where the Structured Light (SL) patterns are partitioned into a number of non‐overlapping blocks that each represent a codeword for explicitly encoding the projector's corresponding pixel address, resulting in an efficient computation for both constructing the SL images and for obtaining the projector‐camera pixel correspondences.