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

2026-04-17

AI Summary

This work addresses the challenge of non-line-of-sight (NLOS) reconstruction using low-power, consumer-grade LiDAR systems, which are typically constrained by limited hardware capabilities that hinder traditional approaches. To overcome this limitation, the authors propose a data-driven NLOS sensing method and introduce DENALI, the first large-scale real-world spatio-temporal histogram dataset specifically designed for consumer LiDARs. DENALI encompasses diverse object geometries, positions, lighting conditions, and spatial resolutions. Experimental results demonstrate that the proposed method effectively recovers information about occluded objects from multi-bounce light signals, achieving high-fidelity NLOS reconstruction. Furthermore, the study reveals critical fidelity gaps in sim-to-real transfer, thereby establishing both a foundational dataset and a methodological framework for advancing NLOS perception with low-cost LiDAR systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Non-Line-of-Sight
• LiDAR
• Hidden Object Perception
• Consumer Sensors
• Spatial Reasoning

Innovation

• non-line-of-sight
• low-cost LiDAR
• time-resolved histograms
• data-driven perception
• DENALI dataset