Is Your LiDAR Placement Optimized for 3D Scene Understanding?

TL;DR

This paper introduces Place3D, a comprehensive pipeline for optimizing multi-LiDAR placement in autonomous driving, including a new evaluation metric, an optimization strategy, and a large dataset for diverse conditions.

Contribution

The paper presents a novel framework with a new surrogate metric, an optimization method, and a large multi-condition dataset for multi-LiDAR placement in 3D scene understanding.

Findings

Optimized LiDAR placements outperform baselines in segmentation and detection.

The M-SOG metric effectively evaluates LiDAR placement quality.

The dataset covers both clean and adverse conditions, enhancing robustness.

Abstract

The reliability of driving perception systems under unprecedented conditions is crucial for practical usage. Latest advancements have prompted increasing interest in multi-LiDAR perception. However, prevailing driving datasets predominantly utilize single-LiDAR systems and collect data devoid of adverse conditions, failing to capture the complexities of real-world environments accurately. Addressing these gaps, we proposed Place3D, a full-cycle pipeline that encompasses LiDAR placement optimization, data generation, and downstream evaluations. Our framework makes three appealing contributions. 1) To identify the most effective configurations for multi-LiDAR systems, we introduce the Surrogate Metric of the Semantic Occupancy Grids (M-SOG) to evaluate LiDAR placement quality. 2) Leveraging the M-SOG metric, we propose a novel optimization strategy to refine multi-LiDAR placements. 3) Centered around the theme of multi-condition multi-LiDAR perception, we collect a 280,000-frame dataset from both clean and adverse conditions. Extensive experiments demonstrate that LiDAR placements optimized using our approach outperform various baselines. We showcase exceptional results in both LiDAR semantic segmentation and 3D object detection tasks, under diverse weather and sensor failure conditions.