A Multi-Object Detection Method Based on Adaptive Feature Adjustment of 3D Point Cloud in Indoor Scenes

Abstract

Due to the complexity and diversity of indoor environment objects and interference occlusions, the accuracy of multi-object target detection based on 3D point cloud is limited. To address this issue, we present a multi-target detection method based on adaptive feature adjustment (AFA) of 3D point cloud. First, our method preprocesses the dataset and constructs a backbone module. Afterwards, our method uses an improved PointNet[Formula: see text] network for feature adaptive learning, where an AFA module is added to learn the influence relationship between point pairs. The proposed method then establishes the relationship between contexts in the local point set area and extracts the feature of point cloud. Using the idea of Hough voting, our method can generate some votes close to the particle. Using these votes to generate proposal, the proposed method adds CBAM attention mechanisms to both modules of voting and proposal, which can fuse the feature information of the channel and expand the receptive field in space. Our method can enhance the important features and weaken the unimportant features, making the extracted features more directional and enhancing the expressiveness of the network. Finally, the generated results are visualized to complete the multi-target detection of 3D point cloud. To verify the effectiveness of our proposed method, two large datasets with real 3D scanning, scanNet2 and SunRGB-D, are used for training the network. The experimental results show that the proposed method can improve the effectiveness of point cloud target detection in indoor scenes, getting a higher detection accuracy.