RESUMO
The recent years have given rise to a large number of techniques for "looking around corners", i.e., for reconstructing or tracking occluded objects from 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 method for calibrating time-of-flight-based non-line-of-sight imaging systems that relies on mirrors as known targets. A roughly determined initialization is refined in order to optimize for 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.
RESUMO
Based on a parallel scalable library for Coulomb interactions in particle systems, a comparison between the fast multipole method (FMM), multigrid-based methods, fast Fourier transform (FFT)-based methods, and a Maxwell solver is provided for the case of three-dimensional periodic boundary conditions. These methods are directly compared with respect to complexity, scalability, performance, and accuracy. To ensure comparable conditions for all methods and to cover typical applications, we tested all methods on the same set of computers using identical benchmark systems. Our findings suggest that, depending on system size and desired accuracy, the FMM- and FFT-based methods are most efficient in performance and stability.
