论文标题
LIDAR序列的在线分割：数据集和算法
Online Segmentation of LiDAR Sequences: Dataset and Algorithm
论文作者
论文摘要
自动驾驶汽车广泛使用了屋顶安装的旋转激光雷达传感器。但是，用于LIDAR序列分割的大多数语义数据集和算法都以$ 360^\ circ $框架运行，从而导致收购潜伏期与实时应用程序不相容。为了解决此问题，我们首先引入Helixnet，这是一个10亿美元的点数据集，具有细粒度的标签，时间戳和传感器旋转信息，以准确评估分割算法的实时准备就绪。其次，我们提出了Helix4D，这是一种专门设计用于旋转激光雷达序列的紧凑而有效的时空变压器结构。 Helix4D在与全传感器旋转的一部分相对应的采集切片上运行，从而大大降低了总延迟。 Helix4D与Helixnet和Semantickitti上最佳的分段算法达到准确性，其延迟降低了$ 5 \ times $，而型号大小的$ 50 \ times $ \ times $。代码和数据可在以下网址获得：https：//romainloiseau.fr/helixnet
Roof-mounted spinning LiDAR sensors are widely used by autonomous vehicles. However, most semantic datasets and algorithms used for LiDAR sequence segmentation operate on $360^\circ$ frames, causing an acquisition latency incompatible with real-time applications. To address this issue, we first introduce HelixNet, a $10$ billion point dataset with fine-grained labels, timestamps, and sensor rotation information necessary to accurately assess the real-time readiness of segmentation algorithms. Second, we propose Helix4D, a compact and efficient spatio-temporal transformer architecture specifically designed for rotating LiDAR sequences. Helix4D operates on acquisition slices corresponding to a fraction of a full sensor rotation, significantly reducing the total latency. Helix4D reaches accuracy on par with the best segmentation algorithms on HelixNet and SemanticKITTI with a reduction of over $5\times$ in terms of latency and $50\times$ in model size. The code and data are available at: https://romainloiseau.fr/helixnet
