DENALI：一个利用低成本激光雷达实现非视域空间推理的数据集
DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs
提供了首个大规模真实世界低成本的消费级激光雷达时空直方图数据集，可用于训练和评测数据驱动的非视域感知方法，并揭示了仿真到真实迁移的差距，为后续研究提供基准。
原文依据 We present DENALI, the first large-scale real-world dataset of space-time histograms from low-cost LiDARs capturing hidden objects.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论移动设备和机器人中的消费级激光雷达通常每个像素输出一个深度值。然而在内部，它们记录包含直接和多跳光返回的完整时间分辨直方图；这些多跳返回编码了丰富的非视域线索，能够实现对场景中隐藏物体的感知。然而，消费级激光雷达的严重硬件限制使得传统方法难以进行非视域重建。在本工作中，我们推动一个互补的方向：通过数据驱动推理使低成本激光雷达实现非视域感知。我们提出了DENALI，这是首个大规模真实世界的低成本激光雷达时空直方图数据集，用于捕获隐藏物体。我们采集了72,000个隐藏物体场景的时间分辨激光雷达直方图，涵盖了不同的物体形状、位置、光照条件和空间分辨率。使用我们的数据集，我们展示了消费级激光雷达能够实现准确的数据驱动非视域感知。我们进一步确定了限制性能的关键场景和建模因素，以及阻碍当前仿真到真实迁移的仿真保真度差距，从而推动未来朝着使用消费级激光雷达的可扩展非视域视觉发展。
英文原文摘要 1192 字符
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.