RESUMEN
We demonstrate a fully submerged underwater LiDAR transceiver system based on single-photon detection technologies. The LiDAR imaging system used a silicon single-photon avalanche diode (SPAD) detector array fabricated in complementary metal-oxide semiconductor (CMOS) technology to measure photon time-of-flight using picosecond resolution time-correlated single-photon counting. The SPAD detector array was directly interfaced to a Graphics Processing Unit (GPU) for real-time image reconstruction capability. Experiments were performed with the transceiver system and target objects immersed in a water tank at a depth of 1.8 meters, with the targets placed at a stand-off distance of approximately 3 meters. The transceiver used a picosecond pulsed laser source with a central wavelength of 532 nm, operating at a repetition rate of 20 MHz and average optical power of up to 52 mW, dependent on scattering conditions. Three-dimensional imaging was demonstrated by implementing a joint surface detection and distance estimation algorithm for real-time processing and visualization, which achieved images of stationary targets with up to 7.5 attenuation lengths between the transceiver and the target. The average processing time per frame was approximately 33 ms, allowing real-time three-dimensional video demonstrations of moving targets at ten frames per second at up to 5.5 attenuation lengths between transceiver and target.
RESUMEN
In this article, we present a new algorithm for fast, online 3D reconstruction of dynamic scenes using times of arrival of photons recorded by single-photon detector arrays. One of the main challenges in 3D imaging using single-photon lidar in practical applications is the presence of strong ambient illumination which corrupts the data and can jeopardize the detection of peaks/surface in the signals. This background noise not only complicates the observation model classically used for 3D reconstruction but also the estimation procedure which requires iterative methods. In this work, we consider a new similarity measure for robust depth estimation, which allows us to use a simple observation model and a non-iterative estimation procedure while being robust to mis-specification of the background illumination model. This choice leads to a computationally attractive depth estimation procedure without significant degradation of the reconstruction performance. This new depth estimation procedure is coupled with a spatio-temporal model to capture the natural correlation between neighboring pixels and successive frames for dynamic scene analysis. The resulting online inference process is scalable and well suited for parallel implementation. The benefits of the proposed method are demonstrated through a series of experiments conducted with simulated and real single-photon lidar videos, allowing the analysis of dynamic scenes at 325 m observed under extreme ambient illumination conditions.
RESUMEN
Non-line-of-sight (NLOS) imaging is a rapidly growing field seeking to form images of objects outside the field of view, with potential applications in autonomous navigation, reconnaissance, and even medical imaging. The critical challenge of NLOS imaging is that diffuse reflections scatter light in all directions, resulting in weak signals and a loss of directional information. To address this problem, we propose a method for seeing around corners that derives angular resolution from vertical edges and longitudinal resolution from the temporal response to a pulsed light source. We introduce an acquisition strategy, scene response model, and reconstruction algorithm that enable the formation of 2.5-dimensional representations-a plan view plus heights-and a 180∘ field of view for large-scale scenes. Our experiments demonstrate accurate reconstructions of hidden rooms up to 3 meters in each dimension despite a small scan aperture (1.5-centimeter radius) and only 45 measurement locations.
RESUMEN
Single-photon lidar has emerged as a prime candidate technology for depth imaging through challenging environments. Until now, a major limitation has been the significant amount of time required for the analysis of the recorded data. Here we show a new computational framework for real-time three-dimensional (3D) scene reconstruction from single-photon data. By combining statistical models with highly scalable computational tools from the computer graphics community, we demonstrate 3D reconstruction of complex outdoor scenes with processing times of the order of 20 ms, where the lidar data was acquired in broad daylight from distances up to 320 metres. The proposed method can handle an unknown number of surfaces in each pixel, allowing for target detection and imaging through cluttered scenes. This enables robust, real-time target reconstruction of complex moving scenes, paving the way for single-photon lidar at video rates for practical 3D imaging applications.