DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs

TL;DR

This paper introduces DENALI, a large-scale dataset of low-cost LiDAR histograms for non-line-of-sight perception, demonstrating data-driven inference can enable hidden object detection despite hardware limitations.

Contribution

The work provides the first extensive real-world dataset of low-cost LiDAR histograms for NLOS perception and analyzes factors affecting performance and simulation-to-real transfer.

Findings

Consumer LiDARs can enable accurate NLOS perception with data-driven methods.

The dataset includes 72,000 scenes with diverse objects and conditions.

Key scene and modeling factors limit NLOS performance and transferability.

Abstract

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.