根据物体特性动态调整帧数，提升激光雷达3D目标检测精度
VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation
- 按物体速度和点密度动态决定聚合帧数
- 在Waymo数据集上超越现有单阶段检测器
- 不依赖特定架构，可适配多种检测模型
输入聚合是当前先进激光雷达3D目标检测器常用的技术，但增加聚合帧数会带来收益递减甚至性能下降，因不同物体对帧数的响应各异。为此，我们提出一种高效自适应方法——可变聚合检测（VADet）。不同于固定帧数聚合整个场景，VADet对每个物体独立确定聚合帧数，依据其速度、点密度等观测属性。该方法有效缓解了固定聚合带来的固有权衡，且不依赖具体网络结构。我们在三个主流单阶段检测器上应用VADet，实现在Waymo数据集上的最先进性能。
原文摘要 · Abstract (English)
Input aggregation is a simple technique used by state-of-the-art LiDAR 3D object detectors to improve detection. However, increasing aggregation is known to have diminishing returns and even performance degradation, due to objects responding differently to the number of aggregated frames. To address this limitation, we propose an efficient adaptive method, which we call Variable Aggregation Detection (VADet). Instead of aggregating the entire scene using a fixed number of frames, VADet performs aggregation per object, with the number of frames determined by an object's observed properties, such as speed and point density. VADet thus reduces the inherent trade-offs of fixed aggregation and is not architecture specific. To demonstrate its benefits, we apply VADet to three popular single-stage detectors and achieve state-of-the-art performance on the Waymo dataset.
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