TY  - JOUR
T1  - Study on Point Cloud Based 3D Object Detection for Autonomous Driving
AU  - Cheong, Youngjae 
AU  - Jun, Woomin 
AU  - Lee, Sungjin 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2024
DA  - 2024/1/1
DO  - 10.7840/kics.2024.49.1.31
KW  - Autonomous Driving
KW  - Point Cloud
KW  - 3D Object Detection
KW  - Data Augmentation
AB  - For the commercialization of autonomous vehicles, precise perception based on three-dimensional (3D) spatial recognition is imperative. While cameras offer valuable insights, their perception capabilities are inherently limited for comprehensive 3D spatial awareness. Therefore, the integration of LIDAR-based spatial recognition technology is indispensable. This study delved into methods for augmenting point cloud data to maximize the accuracy of LIDAR-based 3D Object Detection. Through this point cloud augmentation approach, techniques such as Jitter, Uniform Sampling, Random Sampling, Scaling, and Translation were employed and analyzed for their impact on detection accuracy. Furthermore, we explored optimal combinations of these techniques to amplify the precision of 3D Object Detection. Experimental outcomes, benchmarked against the KITTI dataset, showcased an improvement in the average precision (AP) by approximately 0.5-0.8. In addition, it was discerned that adopting distinct augmentation techniques, in particular Jitter, for different classes yielded enhanced results.