论文标题
基于激光雷达的3D对象检测的成本感知评估和模型缩放
Cost-Aware Evaluation and Model Scaling for LiDAR-Based 3D Object Detection
论文作者
论文摘要
大量的研究工作专门用于基于激光雷达的3D对象检测，经验性能得到了显着改善。尽管进展令人鼓舞，但我们观察到一个被忽视的问题：在相同成本（例如推理潜伏期）下比较不同的3D检测器并不常见的做法。这使得很难量化最近提出的建筑设计带来的真正性能增长。这项工作的目的是对基于激光雷达的3D对象探测器进行成本感知评估。具体而言，我们专注于第二个基于网格的单阶段检测器，并通过扩展其原始体系结构来分析其在不同成本下的性能。然后，我们将缩放第二个家族与最近的3D检测方法（例如Voxel R-CNN和PV-RCNN ++）进行了比较。结果令人惊讶。我们发现，如果允许使用相同的延迟，第二可以匹配Waymo Open DataSet上当前最新方法的PV-RCNN ++的性能。 Scaled Second也很容易胜过过去一年中发布的许多最近发布的3D检测方法。我们建议未来的研究控制其经验比较中的推论成本，并在提出新颖的3D检测方法时将缩放量的家族作为强大的基线。
Considerable research effort has been devoted to LiDAR-based 3D object detection and empirical performance has been significantly improved. While progress has been encouraging, we observe an overlooked issue: it is not yet common practice to compare different 3D detectors under the same cost, e.g., inference latency. This makes it difficult to quantify the true performance gain brought by recently proposed architecture designs. The goal of this work is to conduct a cost-aware evaluation of LiDAR-based 3D object detectors. Specifically, we focus on SECOND, a simple grid-based one-stage detector, and analyze its performance under different costs by scaling its original architecture. Then we compare the family of scaled SECOND with recent 3D detection methods, such as Voxel R-CNN and PV-RCNN++. The results are surprising. We find that, if allowed to use the same latency, SECOND can match the performance of PV-RCNN++, the current state-of-the-art method on the Waymo Open Dataset. Scaled SECOND also easily outperforms many recent 3D detection methods published during the past year. We recommend future research control the inference cost in their empirical comparison and include the family of scaled SECOND as a strong baseline when presenting novel 3D detection methods.
