Adaptive Multiscale Attention Feature Aggregation for Multi-Modal 3D Occluded Object Detection

Abstract

Accurate perception and understanding of the three-dimensional environment is crucial for autonomous vehicles to navigate efficiently and make wise decisions. However, in complex real-world scenarios, the information obtained by a single-modal sensor is often incomplete, severely affecting the detection accuracy of occluded targets. To address this issue, this paper proposes a novel adaptive multi-scale attention aggregation strategy, efficiently fusing multi-scale feature representations of heterogeneous data to accurately capture the shape details and spatial relationships of targets in three-dimensional space. This strategy utilises learnable sparse keypoints to dynamically align heterogeneous features in a data-driven manner, adaptively modelling the cross-modal mapping relationships between keypoints and their corresponding multi-scale image features. Given the importance of accurately obtaining the three-dimensional shape information of targets for understanding the size and rotation pose of occluded targets, this paper adopts a shape prior knowledge-based constraint method and data augmentation strategy to guide the model to more accurately perceive the complete three-dimensional shape and rotation pose of occluded targets. Experimental results show that our proposed model achieves 2.15%, 3.24% and 2.75% improvement in 3D_R40 mAP score under the easy, moderate and hard difficulty levels compared to MVXNet, significantly enhancing the detection accuracy and robustness of occluded targets in complex scenarios.