Abstract
Learning and understanding 3D point clouds with convolutional networks is challenging due to the irregular and unordered data format. Reviewing existing network models based on PointNet [13] and PointNet++ [14], they resample in different regions and explore not enough due to the irregularity and sparsity of the geometric structures. In this paper, we proposed a double-ball model embedded in the hierarchical network(DbNet) that directly extracts the features from the point clouds. This method avoids overlapping and better captures the local neighborhood geometry of each point. Double-ball model has two key steps: double-ball query and building features graph. Double-ball query avoids the resampling problem caused by the simple ball query. Building features graph takes angular features and edge features of point clouds into consideration. This method has no requirements for translation and rotation with the object. We apply it to 3D shapes classification and segmentation. And experiments on two benchmarks show that the suggested network outperforms the models based on PointNet/PointNet++ and is able to provide state of the art results.
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Shen, M., Gao, Y., Qiu, J. (2020). DbNet: Double-Ball Model for Processing Point Clouds. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2020. Lecture Notes in Computer Science(), vol 12221. Springer, Cham. https://doi.org/10.1007/978-3-030-61864-3_27
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