Masking Graph Cross-Convolution Network for Multispectral Point Cloud Classification

Wu Y., Wan J.

Duan P., Shan T., Kang X., Li S.

Zhang C., Qu D., Du L., Yang K.

Wang Z., Chen H., Liu J., Qin J., Sheng Y., Yang L.

Wang Q., Wang M., Huang J., Liu T., Shen T., Gu Y.

Wu X., Jiang L., Wang P., Liu Z., Liu X., Qiao Y., Ouyang W., He T., Zhao H.

Wang Q., Zhang Z., Huang J., Shen T., Gu Y.

Wang Q., Wang M., Zhang Z., Song J., Zeng K., Shen T., Gu Y.

The multitude of airborne point clouds limits the point cloud processing efficiency. Superpoints are grouped based on similar points, which can effectively alleviate the demand for computing resources and improve processing efficiency. However, existing superpoint segmentation methods focus only on local geometric structures, resulting in inconsistent spectral features of points within a superpoint. Such feature inconsistencies degrade the performance of subsequent tasks. Thus, this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation (GSI-SS). Specifically, a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented superpoints. Following the formation of the primary superpoints, an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is proposed. Experiments are conducted on two real multispectral point cloud datasets, and the proposed method achieved higher recall, precision, F score, and lower global consistency and feature classification errors. The experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

Yang J., Luo B., Gan R., Wang A., Shi S., Du L.

Lin J., Gao F., Shi X., Dong J., Du Q.

Wang Q., Zhang Z., Chen X., Wang Z., Song J., Shen T.

Over an extended period, considerable research has focused on elaborated mapping in navigation systems. Multispectral point clouds containing both spatial and spectral information play a crucial role in remote sensing by enabling more accurate land cover classification and the creation of more accurate maps. However, existing graph-based methods often overlook the individual characteristics and information patterns in these graphs, leading to a convoluted pattern of information aggregation and a failure to fully exploit the spatial–spectral information to classify multispectral point clouds. To address these limitations, this paper proposes a deep spatial graph convolution network with adaptive spectral aggregated residuals (DSGCN-ASR). Specifically, the proposed DSGCN-ASR employs spatial graphs for deep convolution, using spectral graph aggregated information as residuals. This method effectively overcomes the limitations of shallow networks in capturing the nonlinear characteristics of multispectral point clouds. Furthermore, the incorporation of adaptive residual weights enhances the use of spatial–spectral information, resulting in improved overall model performance. Experimental validation was conducted on two datasets containing real scenes, comparing the proposed DSGCN-ASR with several state-of-the-art graph-based methods. The results demonstrate that DSGCN-ASR better uses the spatial–spectral information and produces superior classification results. This study provides new insights and ideas for the joint use of spatial and spectral information in the context of multispectral point clouds.

Zhang H., Wang C., Tian S., Lu B., Zhang L., Ning X., Bai X.

In recent years, point cloud representation has become one of the research hotspots in the field of computer vision, and has been widely used in many fields, such as autonomous driving, virtual reality, robotics, etc. Although deep learning techniques have achieved great success in processing regular structured 2D grid image data, there are still great challenges in processing irregular, unstructured point cloud data. Point cloud classification is the basis of point cloud analysis, and many deep learning-based methods have been widely used in this task. Therefore, the purpose of this paper is to provide researchers in this field with the latest research progress and future trends. First, we introduce point cloud acquisition, characteristics, and challenges. Second, we review 3D data representations, storage formats, and commonly used datasets for point cloud classification. We then summarize deep learning-based methods for point cloud classification and complement recent research work. Next, we compare and analyze the performance of the main methods. Finally, we discuss some challenges and future directions for point cloud classification.

Zhang Q., Peng Y., Zhang Z., Li T.

Schult J., Engelmann F., Hermans A., Litany O., Tang S., Leibe B.

Duan P., Kang X., Ghamisi P., Li S.

Xian J., Ma J., Chen X., Wu H., Mei X., Zhang Y., Wang W.