We present a robust real-time LiDAR 3D object detector that leverages heteroscedastic
aleatoric uncertainties to significantly improve its detection performance. A multi-loss …

Autonomous driving datasets are often skewed and in particular, lack training data for
objects at farther distances from the ego vehicle. The imbalance of data causes a …

In this paper, we present LaserNet, a computationally efficient method for 3D object
detection from LiDAR data for autonomous driving. The efficiency results from processing …

In this paper, we propose a novel form of the loss function to increase the performance of
LiDAR-based 3D object detection and obtain more explainable and convincing uncertainty …

Contemporary LiDAR-based 3D object detection methods mostly focus on single-domain
learning or cross-domain adaptive learning. However, for autonomous driving systems …

The inherent ambiguity in ground-truth annotations of 3D bounding boxes, caused by
occlusions, signal missing, or manual annotation errors, can confuse deep 3D object …

To achieve autonomous driving, developing 3D detection fusion methods, which aim to fuse
the camera and LiDAR information, has draw great research interest in recent years. As a …

Supervised 3D Object Detection models have been displaying increasingly better
performance in single-domain cases where the training data comes from the same …

This work presents a probabilistic deep neural network that combines LiDAR point clouds
and RGB camera images for robust, accurate 3D object detection. We explicitly model …

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 …