Deep Learning Based 3D Object Detection and Pose Estimation

Computer Engineering

In recent years, computer vision has seen remarkable progress in 3D object detection and 6D pose estimation, both of which are fundamental to intelligent perception systems. While 3D object detection focuses on identifying an object’s position, size, and orientation, 6D pose estimation extends this by predicting the complete 3D translation and rotation vectors. The successful combination of these techniques has significant implications in fields such as autonomous driving, robotics, and augmented reality. Despite extensive research on 3D object detection and pose estimation using RGB images, several challenges such as occlusion, real-time performance, and generalization remain unsolved. 3-D object detection has become essential for autonomous systems, yet the field remains fragmented due to diverse sensor modalities, fusion strategies, and architectural designs. This review aims to unify current approaches by proposing a taxonomy based on fusion granularity, early, mid, and late fusion, and categorizing methods across key architectural families: monocular, LiDAR-only, multi-modal fusion, and transformer-based models. We systematically examine attention mechanisms for contextual and cross-modal modelling, advancements in backbone networks, and solutions for sensor misalignment, calibration issues, and temporal synchronization. Special emphasis is placed on real-world deployment challenges, including occlusion, environmental variability, adverse weather, scalability, and computational efficiency. Results indicate that transformer-based models (e.g., DETR3D, MonoDETR) achieve improved context reasoning and cross-view feature aggregation, outperforming conventional CNN models in Many multi-view and BEV-based tasks. However, they often incur higher computational costs. Fusion-based models demonstrate enhanced robustness to occlusion and sensor discrepancies. Our discussion highlights trade-offs between accuracy, generalization, and real-time inference capabilities, as well as concerns about cost and scalability critical for commercial deployment. This paper provides a comprehensive review of contemporary deep learning-based methods for 3D object detection and 6D pose estimation. It discusses key algorithms, benchmark datasets, evaluation metrics, and the persistent challenges that limit performance. Using autonomous vehicles as a case study, the paper highlights how these models are applied in real-world environments.

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