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Is Your LiDAR Placement Optimized for 3D Scene Understanding?

Ye Li, Lingdong Kong, Hanjiang Hu, Xiaohao Xu, Xiaonan Huang

TL;DR

The Surrogate Metric of the Semantic Occupancy Grids (M-SOG) to evaluate LiDAR placement quality is introduced and a novel optimization strategy to refine multi-LiDAR placements is proposed to refine multi-LiDAR placements.

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.

Is Your LiDAR Placement Optimized for 3D Scene Understanding?

TL;DR

The Surrogate Metric of the Semantic Occupancy Grids (M-SOG) to evaluate LiDAR placement quality is introduced and a novel optimization strategy to refine multi-LiDAR placements is proposed to refine multi-LiDAR placements.

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.
Paper Structure (43 sections, 4 theorems, 24 equations, 16 figures, 14 tables, 1 algorithm)

This paper contains 43 sections, 4 theorems, 24 equations, 16 figures, 14 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Place3D: A Full-Cycle LiDAR Placement Pipeline
  4. Preliminary Formulation of Surrogate Metric
  5. M-SOG: Surrogate Metric of Semantic Occupancy Grids
  6. Sensor Configuration Optimization
  7. Theoretical Analysis
  8. Experiments
  9. Benchmark Setups
  10. Comparative Study
  11. Ablation Study
  12. Conclusion
  13. The Place3D Dataset
  14. Statistics
  15. Data Collection
  16. ...and 28 more sections

Key Result

Theorem 1

Given the continuous objective function $G: \mathbb{R}^n\to \mathbb{R}$ with Lipschitz constant $k_G$ w.r.t. input $\textbf{u}\in \mathcal{U} \subset \mathbb{R}^n$ under $\ell_2$ norm, suppose over a $\delta$-density Grids subset $S \subset \mathcal{U}$, the distance between the maximal and minimal where $\textbf{u}^*$ is the global optima over $\mathcal{U}$.

Figures (16)

  • Figure 1: Place3Dpipeline for multi-LiDAR placement optimization. We first utilize the semantic point cloud synthesized in CARLA (a) to generate Probabilistic SOG (b) and obtain voxels covered by LiDAR rays to compute M-SOG (c). We propose a CMA-ES-based optimization strategy to maximize M-SOG, finding optimal LiDAR placement (d). To verify the effectiveness of our LiDAR placement optimization strategy, we contribute a multi-condition multi-LiDAR dataset (e) and evaluate the performance of baselines and optimized placements on both clean and corruption data (f).
  • Figure 2: Visualized LiDAR Placements. We compare the LiDAR placements optimized from our proposed M-SOG metric (for LiDAR semantic segmentation and 3D object detection) and heuristic LiDAR placements utilized by major autonomous vehicle companies (see Appendix Section \ref{['subsec:lidar_config']}).
  • Figure 3: Pipeline of Probabilistic SOG generation. We first merge multiple frames of raw point clouds (a) into dense point clouds (b). Then, we voxelize dense point clouds into SOG, i.e., semantic occupancy grids (c), and traverse all frames of dense point clouds to synthesize probabilistic SOG (d).
  • Figure 4: The correlation between M-SOG and LiDAR semantic segmentation choy2019minkowskitang2020searchingzhou2020polarNetzhu2021cylindrical models performance in the clean condition.
  • Figure 5: Comparisons of M-SOG with S-MIG hu2022investigating using BEVFusion-L liu2023bevfusion and PointPillars lang2019pointpillars.
  • ...and 11 more figures

Theorems & Definitions (6)

  • Theorem 1: Optimality Certification
  • Corollary 1
  • Theorem 2: Optimality Certification
  • proof
  • Corollary 2
  • proof