Zhang, Z.; Bao, Z.; Tian, Q.; Lyu, Z. SAE3D: Set Abstraction Enhancement Network for 3D Object Detection Based Distance Features. Sensors2024, 24, 26.
Zhang, Z.; Bao, Z.; Tian, Q.; Lyu, Z. SAE3D: Set Abstraction Enhancement Network for 3D Object Detection Based Distance Features. Sensors 2024, 24, 26.
Zhang, Z.; Bao, Z.; Tian, Q.; Lyu, Z. SAE3D: Set Abstraction Enhancement Network for 3D Object Detection Based Distance Features. Sensors2024, 24, 26.
Zhang, Z.; Bao, Z.; Tian, Q.; Lyu, Z. SAE3D: Set Abstraction Enhancement Network for 3D Object Detection Based Distance Features. Sensors 2024, 24, 26.
Abstract
With increasing demand from unmanned driving and robotics, more attention has been paid to point cloud-based 3D object accurate detection technology. However, due to the sparseness and irregularity of the point cloud, the most critical problem is how to utilize the relevant features more efficiently. In this paper, we proposed a point-based object detection enhancement network to improve the detection accuracy in the 3D scenes understanding based on the distance features. Firstly, the distance features are extracted from the raw point sets and fused with the raw features about reflectivity of the point cloud to maximizing the use of information in point cloud. Secondly, we enhanced the distance features and raw features that we collectively refer to them as self-features of the key points in Set Abstraction (SA) layers with the self-attention mechanism, so that the foreground points can be better distinguished from the background points. Finally, we revised the group aggregation module in SA layers to enhance the feature aggregation effect of key points. We conducted experiments on the KITTI dataset and nuScenes dataset and the results show the enhancement method proposed in this paper has excellent performance.
Keywords
3D object detection; distance features; SA layer enhancement
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
