A Calibration Scheme for Non-Line-of-Sight Imaging Setups
This addresses the calibration bottleneck for researchers and practitioners in non-line-of-sight imaging, offering a more efficient and accurate alternative to manual methods, though it is incremental as it builds on existing calibration needs.
The paper tackles the problem of calibrating non-line-of-sight imaging setups, which previously relied on manual measurements, by proposing a semi-automatic method using mirrors as targets to optimize spatio-temporal consistency, achieving results that outperform manual calibration in a real-world setup despite challenges like dead pixels and low temporal resolution.
The recent years have given rise to a large number of techniques for "looking around corners", i.e., for reconstructing occluded objects from time-resolved measurements of indirect light reflections off a wall. While the direct view of cameras is routinely calibrated in computer vision applications, the calibration of non-line-of-sight setups has so far relied on manual measurement of the most important dimensions (device positions, wall position and orientation, etc.). In this paper, we propose a semi-automatic method for calibrating such systems that relies on mirrors as known targets. A roughly determined initialization is refined in order to optimize a spatio-temporal consistency. Our system is general enough to be applicable to a variety of sensing scenarios ranging from single sources/detectors via scanning arrangements to large-scale arrays. It is robust towards bad initialization and the achieved accuracy is proportional to the depth resolution of the camera system. We demonstrate this capability with a real-world setup and despite a large number of dead pixels and very low temporal resolution achieve a result that outperforms a manual calibration.