Multi-Scope Feature Extraction for Point Cloud Completion

Wuwei Ma, Qiufeng Wang, Kaizhu Huang, Xiaowei Huang
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Abstract

Point cloud completion aims to predict a complete geometric shape based on a partial point cloud. Recent methods often adopt an encoder-decoder framework, where the encoder extracts global features from the partial points and the decoder utilizes a folding-based model to reform multiple 2D grids to 3D surfaces. To effectively explore local features in the partial points, we propose a multi-scope feature extraction method in the encoder, where multiple k-nearest neighbors are considered in the edge convolution. Furthermore, we integrate the original partial point cloud in the decoder to maintain the given geometric shape information. Finally, we refine those coarse points from the decoder by both the merging and sampling operations to output the final completed point cloud. Extensive experiments verify the effectiveness of the proposed approach where both the multi-scope feature extraction and the integration of partial point cloud improve the performance. Overall, our method achieves better performance than the existing methods in both the Earth Mover's Distance (EMD) and the F-score.
点云补全的多范围特征提取
点云补全的目的是在局部点云的基础上预测完整的几何形状。最近的方法通常采用编码器-解码器框架,其中编码器从部分点提取全局特征,解码器利用基于折叠的模型将多个二维网格转换为三维曲面。为了有效地挖掘局部点的局部特征,我们提出了一种编码器中的多范围特征提取方法,该方法在边缘卷积中考虑了多个k近邻。此外,我们将原始部分点云整合到解码器中,以保持给定的几何形状信息。最后,我们通过合并和采样操作对解码器中的粗点进行细化,以输出最终完成的点云。大量的实验验证了该方法的有效性,其中多范围特征提取和局部点云的集成提高了性能。总体而言,我们的方法在动土距离(EMD)和F-score方面都优于现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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