Real-time Acquisition and Reconstruction of Dynamic Volumes with Neural Structured Illumination

We propose a novel framework for real-time acquisition and reconstruction of temporally-varying 3D phenomena with high quality. The core of our framework is a deep neural network, with an encoder that directly maps to the structured illumination during acquisition, a decoder that predicts a 1D density distribution from single-pixel measurements under the optimized lighting, and an aggregation module that combines the predicted densities for each camera into a single volume. It enables the automatic and joint optimization of physical acquisition and computational reconstruction, and is flexible to adapt to different hardware configurations. The effectiveness of our framework is demonstrated on a lightweight setup with an off-the-shelf projector and one or multiple cameras, achieving a performance of $40$ volumes per second at a spatial resolution of $128^3$. We compare favorably with state-of-the-art techniques in real and synthetic experiments, and evaluate the impact of various factors over our pipeline.