Existing offboard 3D detectors always follow a modular pipeline design to take advantage of
unlimited sequential point clouds. We have found that the full potential of offboard 3D …

Semantic perception is a core building block in autonomous driving, since it provides
information about the drivable space and location of other traffic participants. For learning …

Autonomous vehicles need to understand their surroundings geometrically and semantically
to plan and act appropriately in the real world. Panoptic segmentation of LiDAR scans …

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 …

We propose the SAL (S egment A nything in L idar) method consisting of a text-promptable
zero-shot model for segmenting and classifying any object in Lidar, and a pseudo-labeling …

In this paper, we develop rotation-equivariant neural networks for 4D panoptic
segmentation. 4D panoptic segmentation is a benchmark task for autonomous driving that …

With the rapid advances in autonomous driving, it becomes critical to equip its sensing
system with more holistic 3D perception. However, widely explored tasks like 3D detection …

Scene understanding is crucial for autonomous systems to reliably navigate in the real
world. Panoptic segmentation of 3D LiDAR scans allows us to semantically describe a …

Abstract 4D panoptic segmentation is a challenging but practically useful task that requires
every point in a LiDAR point-cloud sequence to be assigned a semantic class label, and …

Fine-grained scene understanding is essential for autonomous driving. The context around
a vehicle can change drastically while navigating, making it hard to identify and understand …