Shape from grid pattern based on coplanarity constraints for one-shot scanning

Shape acquisition of moving deformable objects with little texture is important for applications such as motion capture of human facial expression. Several techniques using structured light have been proposed. These techniques can be largely categorized into two main types. The first type temporally encodes positional information of a projector's pixels using multiple projected patterns, and the second spatially encodes positional information into areas or color spaces. Although the former technique 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 it is more suitable for capturing dynamic scenes ; however, it often uses complex patterns with various colors, which are susceptible to noise, pattern discontinuity caused by edges, or textures. Thus, achieving dense and stable 3D acquisition for fast-moving and deformable objects remains an open problem. We propose a technique to achieve dense shape reconstruction that requires only a single-frame image of a grid pattern based on coplanarity constraints. With our technique, positional information is not encoded in local regions of a projected pattern, but is distributed over the entire grid pattern, which results in robust image processing and 3D reconstruction. The technique also has the advantage of low computational cost due to its efficient formulation.