Consumer LiDARs in mobile devices and robots typically output a single depth value per pixel. Yet internally, they record full time-resolved histograms containing direct and multi-bounce light returns; these multi-bounce returns encode rich non-line-of-sight (NLOS) cues that can enable perception of hidden objects in a scene. However, severe hardware limitations of consumer LiDARs make NLOS reconstruction with conventional methods difficult. In this work, we motivate a complementary direction: enabling NLOS perception with low-cost LiDARs through data-driven inference. We present DENALI, the first large-scale real-world dataset of space-time histograms from low-cost LiDARs capturing hidden objects. We capture time-resolved LiDAR histograms for 72,000 hidden-object scenes across diverse object shapes, positions, lighting conditions, and spatial resolutions. Using our dataset, we show that consumer LiDARs can enable accurate, data-driven NLOS perception. We further identify key scene and modeling factors that limit performance, as well as simulation-fidelity gaps that hinder current sim-to-real transfer, motivating future work toward scalable NLOS vision with consumer LiDARs.

翻译：消费级激光雷达在移动设备和机器人中通常每个像素输出单一的深度值。然而，其内部会记录包含直接光和多反射光返回的全时域直方图；这些多反射返回信号编码了丰富的非视距线索，可用于感知场景中的隐藏物体。但消费级激光雷达的严苛硬件限制使得传统方法难以实现非视距重建。本研究提出一种互补方向：通过数据驱动推理实现低成本激光雷达的非视距感知。我们推出了DENALI——首个大规模真实世界时空直方图数据集，由捕捉隐藏物体的低成本激光雷达构建。该数据集涵盖了72,000个隐藏物体场景，涉及不同物体形状、位置、光照条件和空间分辨率。基于该数据集，我们证明消费级激光雷达可实现精准的数据驱动非视距感知。本文进一步识别了限制性能的关键场景与建模因素，以及阻碍当前仿真到现实迁移的仿真保真度差距，从而为未来利用消费级激光雷达实现可扩展非视距视觉的研究奠定基础。