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: this https URL

本论文提出一种称为 4D-StOP 的新范式，通过基于投票的中心预测生成时空提议，使用学习到的几何特征对因此生成的轨迹提议进行进一步聚合，从而生成整个空间 - 时间体积的视频级别 4D 场景表示，该方法在 3D 物体分割任务上取得显著提高，超过以往采用高斯概率分布代表时空嵌入的现有最先进方法。在应用于 SemanticKITTI 测试数据集时，4D-StOP 实现了 63.9 LSTQ 的新最高分，比当前表现最佳的端到端可训练方法实现了大于 7% 的提高。