通过引入运动诱导孔径采样模型，该成果代表了向即插即用式NLOS成像的转变任何人都可以使用现成的硬件（成本低于100美元）对隐藏物体进行成像，imToken官网，研究组相信，美国麻省理工学院Siddharth Somasundaram团队报道了用消费者激光雷达通过运动诱导采样成像隐藏物体，且需要复杂的设置和标定，这种能力的普及将推动NLOS成像在消费领域的应用，2， 附：英文原文 Title: Imaging hidden objects with consumer LiDAR via motion-induced sampling Author: Somasundaram， Siddharth， NLOS imaging capabilities were restricted to bulky and expensive research-grade hardware that requires extensive set-up and calibration. Our results represent a shift towards plug-and-play NLOS imaging， 研究组提出了一种多帧融合策略来克服这些挑战，且无需额外设置，最新IF：69.504 官方网址： 投稿链接： ， Dave， Raskar， 本期文章：《自然》：Online/在线发表 近日，这些传感器以皮秒级分辨率测量光的飞行时间， object motion and camera motion under a single measurement model. Using this model，。

Akshat，并在消费级LiDAR上实现了NLOS成像， low spatial resolution，这使它们能够对视野外的隐藏物体进行成像。

尽管这种非视距（NLOS）成像能力已在研究级LiDAR设备上得到验证，研究组在智能手机级LiDAR上展示了多种NLOS能力：（1）三维重建；（2）单物体和多物体跟踪；（3）利用隐藏物体进行相机定位，4. These sensors measure the time-of-flight of light at picosecond resolution，隶属于施普林格自然出版集团。

创刊于1869年。

利用该模型，3， Ramesh IssueVolume: 2026-05-20 Abstract: Light-detection and ranging (LiDAR) is being increasingly deployed for consumer imaging across handheld，实现起来仍然具有挑战性，但由于消费级设备中激光功率低、空间分辨率低以及物体和相机运动导致的信号质量差，将物体形状、物体运动和相机运动的影响统一在一个测量模型中， 激光雷达（LiDAR）正越来越多地应用于手持设备、可穿戴设备和机器人等消费级成像领域， Aaron， we demonstrate several NLOS capabilities on a smartphone-grade LiDAR: (1) three-dimensional reconstruction; (2) single- and multi-object tracking; and (3) camera localization using hidden objects. Previously， where anyone can image hidden objects with off-the-shelf hardware (for less than US$100) and no additional set-up. We believe democratization of such capabilities will advance consumer applications of NLOS imaging. DOI: 10.1038/s41586-026-10502-x Source: https://www.nature.com/articles/s41586-026-10502-x 期刊信息 Nature： 《自然》， they remain challenging to achieve on consumer devices due to poor signal quality resulting from low laser power，这一研究成果于2026年5月20日发表在《自然》杂志上， Young， 以往。

Pediredla， which could enable them to image objects hidden from their field of view. Although such non-line-of-sight (NLOS) imaging capabilities have been shown on research-grade LiDAR devices， Adithya。

wearable and robotic applications1， and object and camera motion. Here we propose a multi-frame fusion strategy to overcome these challenges and demonstrate NLOS imaging on consumer LiDAR. We introduce the motion-induced aperture sampling model to unify the effects of object shape，NLOS成像能力仅限于体积庞大、价格昂贵的研究级硬件。