Looking Around Flatland: End-to-End 2D Real-Time NLOS Imaging

IF 4.2 2区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
María Peña;Diego Gutierrez;Julio Marco
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引用次数: 0

Abstract

Time-gated non-line-of-sight (NLOS) imaging methods reconstruct scenes hidden around a corner by inverting the optical path of indirect photons measured at visible surfaces. These methods are, however, hindered by intricate, time-consuming calibration processes involving expensive capture hardware. Simulation of transient light transport in synthetic 3D scenes has become a powerful but computationally-intensive alternative for analysis and benchmarking of NLOS imaging methods. NLOS imaging methods also suffer from high computational complexity. In our work, we rely on dimensionality reduction to provide a real-time simulation framework for NLOS imaging performance analysis. We extend steady-state light transport in self-contained 2D worlds to take into account the propagation of time-resolved illumination by reformulating the transient path integral in 2D. We couple it with the recent phasor-field formulation of NLOS imaging to provide an end-to-end simulation and imaging pipeline that incorporates different NLOS imaging camera models. Our pipeline yields real-time NLOS images and progressive refinement of light transport simulations. We allow comprehensive control on a wide set of scene, rendering, and NLOS imaging parameters, providing effective real-time analysis of their impact on reconstruction quality. We illustrate the effectiveness of our pipeline by validating 2D counterparts of existing 3D NLOS imaging experiments, and provide an extensive analysis of imaging performance including a wider set of NLOS imaging conditions, such as filtering, reflectance, and geometric features in NLOS imaging setups.
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来源期刊
IEEE Transactions on Computational Imaging
IEEE Transactions on Computational Imaging Mathematics-Computational Mathematics
CiteScore
8.20
自引率
7.40%
发文量
59
期刊介绍: The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs. Topics of interest will include advanced algorithms and mathematical techniques, model-based data inversion, methods for image and signal recovery from sparse and incomplete data, techniques for non-traditional sensing of image data, methods for dynamic information acquisition and extraction from imaging sensors, software and hardware for efficient computation in imaging systems, and highly novel imaging system design.
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