  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.
