A Survey of Deep Learning-Driven 3D Object Detection: Sensor Modalities, Technical Architectures, and Applications

This review presents a comprehensive survey on deep learning-driven 3D object detection, focusing on the synergistic innovation between sensor modalities and technical architectures. Through a dual-axis "sensor modality-technical architecture" classification framework, it systematically analyzes detection methods based on RGB cameras, LiDAR, and multimodal fusion. From the sensor perspective, the study reveals the evolutionary paths of monocular depth estimation optimization, LiDAR point cloud processing from voxel-based to pillar-based modeling, and three-level cross-modal fusion paradigms (data-level alignment, feature-level interaction, and result-level verification). Regarding technical architectures, the paper examines structured representation optimization in traditional convolutional networks, spatiotemporal modeling breakthroughs in bird's-eye view (BEV) methods, voxel-level modeling advantages of occupancy networks for irregular objects, and dynamic scene understanding capabilities of temporal fusion architectures. The applications in autonomous driving and agricultural robotics are discussed, highlighting future directions including depth perception enhancement, open-scene modeling, and lightweight deployment to advance 3D perception systems toward higher accuracy and stronger generalization.