Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization
This work addresses the need for faster, real-time non-line-of-sight imaging in applications like rescue operations and autonomous driving, though it appears incremental as it builds on existing regularization techniques.
The paper tackles the problem of long acquisition times in non-line-of-sight imaging by proposing a signal-surface collaborative regularization framework that achieves noise-robust reconstructions with minimal measurements, reporting an acceleration factor of 10000 using only 5x5 confocal measurements.
The non-line-of-sight imaging technique aims to reconstruct targets from multiply reflected light. For most existing methods, dense points on the relay surface are raster scanned to obtain high-quality reconstructions, which requires a long acquisition time. In this work, we propose a signal-surface collaborative regularization (SSCR) framework that provides noise-robust reconstructions with a minimal number of measurements. Using Bayesian inference, we design joint regularizations of the estimated signal, the 3D voxel-based representation of the objects, and the 2D surface-based description of the targets. To our best knowledge, this is the first work that combines regularizations in mixed dimensions for hidden targets. Experiments on synthetic and experimental datasets illustrated the efficiency and robustness of the proposed method under both confocal and non-confocal settings. We report the reconstruction of the hidden targets with complex geometric structures with only $5 \times 5$ confocal measurements from public datasets, indicating an acceleration of the conventional measurement process by a factor of 10000. Besides, the proposed method enjoys low time and memory complexities with sparse measurements. Our approach has great potential in real-time non-line-of-sight imaging applications such as rescue operations and autonomous driving.