Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …

Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …

While current 3D object recognition research mostly focuses on the real-time, onboard
scenario, there are many offboard use cases of perception that are largely under-explored …

Active learning aims to reduce labeling costs by selecting only the most informative samples
on a dataset. Few existing works have addressed active learning for object detection. Most …

Autonomous driving is considered one of the revolutionary technologies shaping humanity's
future mobility and quality of life. However, safety remains a critical hurdle in the way of …

This paper aims for high-performance offline LiDAR-based 3D object detection. We first
observe that experienced human annotators annotate objects from a track-centric …

We present an automatic annotation pipeline to recover 9D cuboids and 3D shapes from pre-
trained off-the-shelf 2D detectors and sparse LIDAR data. Our autolabeling method solves …

Using deep learning, we now have the ability to create exceptionally good semantic
segmentation systems; however, collecting the prerequisite pixel-wise annotations for …

Supervised training of a deep neural network for semantic segmentation of point clouds
requires a large amount of labelled data. Nowadays, it is easy to acquire a huge number of …