用低成本激光雷达数据实现隐藏物体感知，首次构建真实世界大样本数据集。
DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs
- 基于真实采集的时空直方图数据，实现非视距感知。
- 覆盖7.2万组隐藏物体场景，涵盖多种物体形状与光照条件。
- 揭示真实感知瓶颈，推动消费级激光雷达的非视距应用。
手机和机器人中的消费级激光雷达通常每像素仅输出单一深度值，但其内部记录了包含直接光与多跳反射光的全时序直方图。这些多跳光信号蕴含丰富的非视距（NLOS）线索，可用来感知被遮挡物体。然而，消费级激光雷达硬件限制使传统方法难以实现有效重建。本文提出通过数据驱动方式，利用低成本激光雷达实现非视距感知。我们构建了首个大规模真实世界数据集DENALI，包含72,000个隐藏物体场景的时空直方图，涵盖多样化的物体形状、位置、光照条件与空间分辨率。实验表明，消费级激光雷达结合数据驱动方法可实现高精度非视距感知。同时，我们识别出影响性能的关键场景与建模因素，以及当前仿真到真实迁移中的保真度差距，为未来基于消费级激光雷达的可扩展非视距视觉研究提供方向。
原文摘要 · Abstract (English)
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.
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