Authors : Gadi Haritha Rani; Mandapalli Rafath Kumar; Balam Mounica

Volume/Issue : Volume 10 - 2025, Issue 2 - February

Google Scholar : https://tinyurl.com/yck4euax

Scribd : https://tinyurl.com/y4h898hd

DOI : https://doi.org/10.5281/zenodo.14964324

Abstract : Autonomous systems, including self-driving vehicles and robotic navigation, rely heavily on accurate 3D object detection for safe and efficient operation. Traditional vision-based approaches often struggle in low-light or adverse weather conditions, necessitating the integration of LiDAR and depth sensing technologies. This paper explores the latest advancements in AI-driven 3D object detection, leveraging deep learning models such as PointNet, VoxelNet, and Transformer-based architectures. We discuss the role of sensor fusion techniques, where LiDAR and depth cameras complement RGB data for enhanced perception. Additionally, we analyze challenges in real-time processing, occlusion handling, and domain adaptation, while highlighting recent breakthroughs in self-supervised learning and few-shot learning for 3D detection. Experimental results demonstrate the effectiveness of AI-powered models in improving detection accuracy, robustness, and computational efficiency. This study provides a comprehensive overview of AI's role in enhancing perception and decision-making for next-generation autonomous systems.

Keywords : 3D Object Detection, LiDAR(Light Detection and Ranging), Depth Sensing, PointNet, VoxelNet, and Transformer- Based Architectures.

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