论文标题
4D-stop：使用时空对象提案生成和聚合对4D激光雷达的全面分割
4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation
论文作者
论文摘要
在这项工作中，我们提出了一个名为4D-stop的新范式，以应对4D Panoptic LiDAR细分的任务。 4D-Stop首先使用基于投票的中心预测生成时空建议，其中4D卷中的每个点投票给相应的中心。这些曲目提案使用学到的几何特征进一步汇总。 Tracklet聚合方法有效地生成了整个时空量的视频级别4D场景表示。这与现有的端到端可训练的最新方法相反，后者使用以高斯概率分布表示的时空嵌入。与使用高斯概率分布对整个4D体积进行建模相比，我们基于投票的轨迹生成方法后面基于几何特征聚合可以显着改善全盘激光雷达分割质量。 4D-Stop在Semantickitti测试数据集上应用于63.9 LSTQ时，可实现新的最新技术，与当前表现最佳的端到端可训练方法相比，它的分数为63.9 LSTQ。代码和预培训模型可在以下网址提供：https：//github.com/larskreuzberg/4d-stop。
In this work, we present a new paradigm, called 4D-StOP, to tackle the task of 4D Panoptic LiDAR Segmentation. 4D-StOP first generates spatio-temporal proposals using voting-based center predictions, where each point in the 4D volume votes for a corresponding center. These tracklet proposals are further aggregated using learned geometric features. The tracklet aggregation method effectively generates a video-level 4D scene representation over the entire space-time volume. This is in contrast to existing end-to-end trainable state-of-the-art approaches which use spatio-temporal embeddings that are represented by Gaussian probability distributions. Our voting-based tracklet generation method followed by geometric feature-based aggregation generates significantly improved panoptic LiDAR segmentation quality when compared to modeling the entire 4D volume using Gaussian probability distributions. 4D-StOP achieves a new state-of-the-art when applied to the SemanticKITTI test dataset with a score of 63.9 LSTQ, which is a large (+7%) improvement compared to current best-performing end-to-end trainable methods. The code and pre-trained models are available at: https://github.com/LarsKreuzberg/4D-StOP.
