论文标题
通过分离的特征聚合，细节保留的点云完成
Detail Preserved Point Cloud Completion via Separated Feature Aggregation
论文作者
论文摘要
在3D视觉和机器人技术中，点云形状的完成是一个具有挑战性的问题。现有的基于学习的框架利用编码器架构架构从高度编码的全局特征向量恢复完整的形状。尽管全局功能大致代表了3D对象的整体形状，但它将导致在完成过程中丢失形状细节。在这项工作中，我们没有使用全局功能来恢复整个完整的表面，而是探索多级特征的功能和汇总的不同功能，以分别表示已知部分和丢失的部分。我们提出了两种不同的特征聚合策略，称为Global \＆Local特征聚合（GLFA）和残留特征聚合（RFA），以表达两种功能和重建坐标的组合。此外，我们还设计了一个改进组件，以防止生成的点云从不均匀分布和异常值中。在Shapenet数据集上进行了广泛的实验。定性和定量评估表明，我们提出的网络的表现优于当前最新方法，尤其是在细节保存方面。
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.
