Neural SDF for Shadow-Aware Unsupervised Structured Light

Among various active 3D measurement techniques, Structured Light (SL) is one of the most popular methods for its robustness and high accuracy. The ordinary SL system consists of a camera and a projector, and by projecting a pre-defined pattern, we can obtain pixel-to-pixel correspondences between the camera and the projector for triangulation. However, if we lack knowledge of the projected pattern for some reason, e.g., the projected pattern is not as expected due to lens distortion, inaccurate calibration, undesired optical phenomena like inter-reflection, and so on, the accuracy of conventional SL is severely degraded. As a remedy, we propose unsupervised structured light (USSL), which does not explicitly use prior knowledge of the pattern. Inspired by the fact that humans can recognize the scene structure illuminated by an unknown light source (e.g. rotating mirror ball), and some prior works have succeeded in novel-view-synthesis under unknown illumination conditions, we implement USSL on Neural Signed Distance Fields (Neural SDF) pipeline with implicit reflection module powered by a neural network. Additionally, since every SL method causes occlusion (shadow) by pattern projection, we must consider it for accurate shape reconstruction. To this end, we integrate shadow volume rendering into the proposed pipeline. Experiments with synthetic and real datasets are conducted to confirm the feasibility of the proposed method.