Physics to the Rescue: Deep Non-line-of-sight Reconstruction for High-speed Imaging

TL;DR

This paper introduces a deep learning model that incorporates physics priors for high-speed non-line-of-sight imaging, enabling real-time reconstruction with improved robustness and quality.

Contribution

A novel deep neural network integrating wave propagation and volume rendering physics priors for robust, high-speed NLOS image reconstruction trained on synthetic data.

Findings

Achieves over 5 captures per second inference speed.

Outperforms prior methods on synthetic and real data.

Generalizes well despite training only on synthetic data.

Abstract

Computational approach to imaging around the corner, or non-line-of-sight (NLOS) imaging, is becoming a reality thanks to major advances in imaging hardware and reconstruction algorithms. A recent development towards practical NLOS imaging, Nam et al. demonstrated a high-speed non-confocal imaging system that operates at 5Hz, 100x faster than the prior art. This enormous gain in acquisition rate, however, necessitates numerous approximations in light transport, breaking many existing NLOS reconstruction methods that assume an idealized image formation model. To bridge the gap, we present a novel deep model that incorporates the complementary physics priors of wave propagation and volume rendering into a neural network for high-quality and robust NLOS reconstruction. This orchestrated design regularizes the solution space by relaxing the image formation model, resulting in a deep model that generalizes well on real captures despite being exclusively trained on synthetic data. Further, we devise a unified learning framework that enables our model to be flexibly trained using diverse supervision signals, including target intensity images or even raw NLOS transient measurements. Once trained, our model renders both intensity and depth images at inference time in a single forward pass, capable of processing more than 5 captures per second on a high-end GPU. Through extensive qualitative and quantitative experiments, we show that our method outperforms prior physics and learning based approaches on both synthetic and real measurements. We anticipate that our method along with the fast capturing system will accelerate future development of NLOS imaging for real world applications that require high-speed imaging.