The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …

Scalable systems for automated driving have to reliably cope with an open-world setting.
This means, the perception systems are exposed to drastic domain shifts, like changes in …

We study an unsupervised domain adaptation problem for the semantic labeling of 3D point
clouds, with a particular focus on domain discrepancies induced by different LiDAR sensors …

Object detection from 3D point clouds remains a challenging task, though recent studies
pushed the envelope with the deep learning techniques. Owing to the severe spatial …

3D object detectors based only on LiDAR point clouds hold the state-of-the-art on modern
street-view benchmarks. However, LiDAR-based detectors poorly generalize across …

We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic
segmentation (LiDARNet). Our model can extract both the domain private features and the …

The human brain can effortlessly recognize and localize objects, whereas current 3D object
detection methods based on LiDAR point clouds still report inferior performance for …

Abstract The Point Fractal Network (PF-Net) is a seminal work with capability of completing
the missing regions of point clouds. However, the multi-resolution structure of PF-Net …

In this paper, we introduce Cirrus, a new long-range bi-pattern LiDAR public dataset for
autonomous driving tasks such as 3D object detection, critical to highway driving and timely …

Self-driving cars must detect other traffic participants like vehicles and pedestrians in 3D in
order to plan safe routes and avoid collisions. State-of-the-art 3D object detectors, based on …