研究激光雷达束密度对3D目标检测性能影响，发现训练用密集数据更优。
An Investigation of Beam Density on LiDAR Object Detection Performance
- 融合体素与点云表示的模型在不同束密度下表现更好。
- 在稀疏推理时，用密集数据训练的模型仍保持稳定性能。
- 适合自动驾驶中跨传感器设置部署的检测系统设计。
高精度3D目标检测是自动驾驶的核心，依赖激光雷达提供精确的三维感知。然而，训练与推理阶段的传感器差异（如束密度变化）会导致模型性能显著下降。本文针对束密度引起的域偏移问题开展系统研究，评估多种检测架构。结果表明，结合体素与点云表征的方法在跨域场景中表现更优。进一步分析发现，尽管传统观点认为应匹配训练与推理的束密度，但实验显示：在密集数据上训练的检测器反而对推理时的束密度变化具有更强鲁棒性。这表明束密度带来的域偏移需与其他域偏移共同考虑，且高密度数据训练能有效缓解此问题。
原文摘要 · Abstract (English)
Accurate 3D object detection is a critical component of autonomous driving, enabling vehicles to perceive their surroundings with precision and make informed decisions. LiDAR sensors, widely used for their ability to provide detailed 3D measurements, are key to achieving this capability. However, variations between training and inference data can cause significant performance drops when object detection models are employed in different sensor settings. One critical factor is beam density, as inference on sparse, cost-effective LiDAR sensors is often preferred in real-world applications. Despite previous work addressing the beam-density-induced domain gap, substantial knowledge gaps remain, particularly concerning dense 128-beam sensors in cross-domain scenarios. To gain better understanding of the impact of beam density on domain gaps, we conduct a comprehensive investigation that includes an evaluation of different object detection architectures. Our architecture evaluation reveals that combining voxel- and point-based approaches yields superior cross-domain performance by leveraging the strengths of both representations. Building on these findings, we analyze beam-density-induced domain gaps and argue that these domain gaps must be evaluated in conjunction with other domain shifts. Contrary to conventional beliefs, our experiments reveal that detectors benefit from training on denser data and exhibit robustness to beam density variations during inference.
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