Object detection in 3D with stereo cameras is an important problem in computer vision, and
is particularly crucial in low-cost autonomous mobile robots without LiDARs. Nowadays …

One of the fundamental challenges to scale self-driving is being able to create accurate high
definition maps (HD maps) with low cost. Current attempts to automate this pro-cess typically …

Data augmentation is an important technique to improve data efficiency and to save labeling
cost for 3D detection in point clouds. Yet, existing augmentation policies have so far been …

In cooperative, connected, and automated mobility (CCAM), the more automated vehicles
can perceive, model, and analyze the surrounding environment, the more they become …

Existing point-cloud based 3D object detectors use convolution-like operators to process
information in a local neighbourhood with fixed-weight kernels and aggregate global context …

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 …

Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007,
autonomous driving has been the most active field of AI applications. Almost at the same …

In the past few years, we have seen great progress in perception algorithms, particular
through the use of deep learning. However, most existing approaches focus on a few …

Semantic map construction under bird's-eye view (BEV) plays an essential role in
autonomous driving. In contrast to camera image, LiDAR provides the accurate 3D …

Abstract 3D object detection from the LiDAR point cloud is fundamental to autonomous
driving. Large-scale outdoor scenes usually feature significant variance in instance scales …