Neural SDF for Shadow-Aware Unsupervised Structured Light

Kazuto Ichimaru, Diego Thomas, Takafumi Iwaguchi, Hiroshi Kawasaki; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 287-296

Among various active 3D measurement techniques Structured Light (SL) is one of the most popular method for its robustness and high accuracy. 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 the 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 phenomenon like inter-reflection and so on the accuracy of conventional SL is severally degraded. As a remedy we propose unsupervised structured light (USSL) which does not explicitly use prior knowledge on the pattern. Inspired by the fact that human can recognize the scene structure illuminated by unknown light source (e.g. rotating mirror ball) and some prior work 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 neural network. Additionally since every SL method causes occlusion (shadow) by pattern projection we must take it into account for accurate shape reconstruction. To this end we integrate shadow volume rendering into the proposed pipeline. Experiments with synthetic and real dataset are conducted to confirm the feasibility of the proposed method.