用梯度下降优化框与点云流的对齐，提升40%交并比。
Registration between Point Cloud Streams and Sequential Bounding Boxes via Gradient Descent
- 构建包含目标与约束的统一目标函数，用梯度下降求解
- 在点云与框序列对齐任务中实现40%的IoU提升
- 适合需要精准时空对齐的自动驾驶场景
本文提出一种将顺序边界框与点云流进行注册的算法。与主流点云配准方法不同，该方法利用边界框的尺寸、形状和时间信息等特性，为配准提供显著支持并带来性能提升。为此，我们建立一个包含最终目标与所有约束的整体目标函数，并通过梯度下降进行优化。实验表明，所提方法在点云流与顺序边界框的配准任务中表现优异，交并比（IoU）提升达40%，且具有更强的鲁棒性。
原文摘要 · Abstract (English)
In this paper, we propose an algorithm for registering sequential bounding boxes with point cloud streams. Unlike popular point cloud registration techniques, the alignment of the point cloud and the bounding box can rely on the properties of the bounding box, such as size, shape, and temporal information, which provides substantial support and performance gains. Motivated by this, we propose a new approach to tackle this problem. Specifically, we model the registration process through an overall objective function that includes the final goal and all constraints. We then optimize the function using gradient descent. Our experiments show that the proposed method performs remarkably well with a 40\% improvement in IoU and demonstrates more robust registration between point cloud streams and sequential bounding boxes
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