Abstract
Classification and segmentation of point clouds have attracted increasing attention in recent years. On the one hand, it is difficult to extract local features with geometric information. On the other hand, how to select more important features correctly also brings challenges to the research. Therefore, the main challenge in classifying and segmenting the point clouds is how to locate the attentional region. To tackle this challenge, we propose a graph-based neural network with an attention pooling strategy (AGNet). In particular, local feature information can be extracted by constructing a topological structure. Compared to existing methods, AGNet can better extract the spatial information with different distances, and the attentional pooling strategy is capable of selecting the most important features of the topological structure. Therefore, our model can aggregate more information to better represent different point cloud features. We conducted extensive experiments on challenging benchmark datasets including ModelNet40 for object classification, as well as ShapeNet Part and S3DIS for segmentation. Both the quantitative and qualitative experiments demonstrated a consistent advantage for the tasks of point set classification and segmentation.
Highlights
Point clouds are the most commonly used representations of 3D data due to the rapid iterative upgrading of sensing equipment and are concerned and applied by more and more researchers for their own unique advantage, which can be acquired by remote sensors [1]or other non-contact methods, such as light, acoustics, and LiDAR [2,3]
ModelNet40 [25] is a dataset for object classification tasks, which is widely used for point cloud analysis due to its clean shapes and well-constructed data
We introduced a novel network on 3D point cloud tasks, called attention-based graph network (AGNet), which is effective for learning on unstructured data point clouds
Summary
Point clouds are the most commonly used representations of 3D data due to the rapid iterative upgrading of sensing equipment and are concerned and applied by more and more researchers for their own unique advantage, which can be acquired by remote sensors [1]or other non-contact methods, such as light, acoustics, and LiDAR [2,3]. Object classification is the basis of object detection, automatic driving, and 3D reconstruction [13]. It plays a key role in many fields, for example face recognition is usually based on an efficient real-time classification algorithm [14]. Point cloud classification is still facing challenges, and more work is urgently needed to solve the current difficulties, which is mainly reflected in the robustness and efficiency that cannot meet the fast-growing needs of industry [15]. The particularity of the point cloud data structure brings huge challenges to the object classification and semantic segmentation task [16]. The collected point clouds can be divided into three types: object point cloud, indoor scene
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