Examenspresentation av Kristian Ceder och Arvid Enliden

Examiner: Lennart Svensson

Abstract:
Since the KITTI dataset was introduced in 2013, research in LiDAR-based object
detection (OD) has increased significantly in popularity. The spike in research has
also led to improved performance, but the predictions of these models are still not
robust or accurate enough to safely be used for autonomous driving. Although the
overall performance of the models is insufficient, long-range predictions is an area
where the need for better accuracy is particularly apparent. The emergence of large
datasets has improved the overall performance of such models, but most datasets
still require data augmentations to help models converge and generalize well to un-
seen data. Many methods used for data augmentation in LiDAR OD have been
accepted as standards in the field since they have been shown to improve overall
performance. Techniques like Ground Truth Database Sampling (GTDS) and Fade
are used without thoroughly analyzing the class-specific effects and results they im-
pose on the models.

This thesis investigates how spatial distribution data augmentations can improve
long-range accuracy of LiDAR OD. This is done by downsampling ground truth
objects from the original GTDS and moving them further away from the sensor.
The goal is to increase diversity in the long-range data while keeping a realistic
spatial distribution of points for objects sampled at that range. Furthermore, a class-
specific analysis of the widely accepted GTDS and Fade augmentations is conducted
to further explore the effects these data augmentations have on the popular nuScenes
dataset. All experiments are conducted on the two common benchmarking models
PointPillars and CenterPoint. The work shows that GTDS can negatively impact the
detection accuracy of less frequent classes in nuScenes, even if the overall accuracy
increases. Moreover, we demonstrate the importance of using Fade in conjunction
with GTDS and how it can mitigate class-specific accuracy losses introduced by
GTDS. Lastly, this thesis analyzes why the long-range sampler ultimately fails at
increasing long-range accuracy.

Welcome!
Kristian, Arvid and Lennart