LiDAR-based 3D detection plays a vital role in autonomous navigation. Surprisingly, although autonomous vehicles (AVs) must detect both near-field objects (for collision avoidance) and far-field objects (for longer-term planning), contemporary benchmarks focus only on near-field 3D detection. However, AVs must detect far-field objects for safe navigation. In this paper, we present an empirical analysis of far-field 3D detection using the long-range detection dataset Argoverse 2.0 to better understand the problem, and share the following insight: near-field LiDAR measurements are dense and optimally encoded by small voxels, while far-field measurements are sparse and are better encoded with large voxels. We exploit this observation to build a collection of range experts tuned for near-vs-far field detection, and propose simple techniques to efficiently ensemble models for long-range detection that improve efficiency by 33% and boost accuracy by 3.2% CDS.

翻译：基于激光雷达的三维检测在自主导航中发挥着关键作用。令人惊讶的是，尽管自动驾驶车辆必须同时检测近场目标（用于避障）和远场目标（用于长期规划），但当前的基准测试仅关注近场三维检测。然而，自动驾驶车辆必须检测远场目标以确保安全导航。本文利用长距离检测数据集Argoverse 2.0对远场三维检测进行了实证分析，以更深入地理解该问题，并提出以下见解：近场激光雷达测量数据密集，适合用小体素进行最优编码，而远场测量数据稀疏，更适合用大体素进行编码。我们利用这一发现构建了一套针对近场与远场检测优化的距离专家模型，并提出了简单有效的模型集成技术以实现长距离检测，该技术将效率提升了33%，并将CDS精度提高了3.2%。