A Study on Target Recognition Algorithm Based on 3D Point Cloud and Feature Fusion

In recent years, with the continuous development of 3D technology, the target recognition technology relying on 3D point cloud has also been gradually attached to people. Compared with 2D images, 3D data acquisition is less affected by environmental factors, and 3D point cloud data can better preserve important information such as coordinates and posture of the target, which can intuitively reflect the location of the target in real space. The 3D point cloud data collected in the actual environment usually contains a large number of noise points, which will have a great impact on the subsequent point cloud processing. Therefore, this paper proposes a point cloud data preprocessing process and method. Moreover, most of the current 3D target recognition algorithms rely too much on a single feature, and the single feature is limited to describe the 3D model, and most of the current algorithms do not take into account the overall and local features at the same time. To address the above problems, this paper proposes a 3D target detection method based on feature fusion, which can efficiently improve the detection performance and recognition accuracy.