Point cloud shape completion is a challenging problem in 3D vision and robotics. Existing learning-based frameworks leverage encoder-decoder architectures to recover the complete shape from a highly encoded global feature vector. Though the global feature can approximately represent the overall shape of 3D objects, it would lead to the loss of shape details during the completion process. In this work, instead of using a global feature to recover the whole complete surface, we explore the functionality of multi-level features and aggregate different features to represent the known part and the missing part separately. We propose two different feature aggregation strategies, named global \& local feature aggregation(GLFA) and residual feature aggregation(RFA), to express the two kinds of features and reconstruct coordinates from their combination. In addition, we also design a refinement component to prevent the generated point cloud from non-uniform distribution and outliers. Extensive experiments have been conducted on the ShapeNet dataset. Qualitative and quantitative evaluations demonstrate that our proposed network outperforms current state-of-the art methods especially on detail preservation.

本文提出了两种特征聚合策略(global&local feature aggregation，GLFA 和 residual feature aggregation，RFA)来表示缺失的部分和已知的部分，并设计了精细化组件以防止生成的点云具有不均匀分布及异常值。在ShapeNet数据集上进行了广泛的实验，定性和定量评估表明我们提出的方法在细节保留方面优于当前最先进的方法。

通过分离特征聚合实现细节保留点云补全