Unveiling the Depths: A Comprehensive Comparison of Monocular Depth Models and LiDAR-Camera 3D Perception

Abstract

In the evolving landscape of autonomous driving and advanced robotics, 3D perception stands as a cornerstone for safe and efficient operation. This domain fundamentally relies on the system's ability to accurately understand its surroundings in three dimensions, enabling tasks such as precise object detection, comprehensive scene understanding, and reliable navigation. This analysis delves into two primary methodologies for achieving 3D perception: monocular depth estimation (MDE) and LiDAR-camera fusion (LCF). While both aim to construct a detailed 3D representation of the environment, they employ distinct sensor modalities and processing paradigms, leading to significant differences in their performance, cost implications, computational demands, and adaptability to various environmental conditions. The comparative evaluation of these approaches heavily relies on established benchmark datasets such as KITTI and nuScenes. These datasets provide a standardized framework for assessing performance using metrics like Root Mean Square Error (RMSE) for depth accuracy, Intersection over Union (IoU) for object detection quality, and inference time for evaluating computational efficiency. Through this rigorous analysis, a clearer understanding of each method's strengths, limitations, and optimal application scenarios emerges, guiding practical recommendations for system design and identifying promising avenues for future research in hybrid perception models.