通过使用反馈网络的结构化特征地图完成点云
Point cloud completion via structured feature maps using a feedback network
摘要：
本文从特征学习的角度出发，解决了点云数据完整化这一具有挑战性的问题。我们的关键观察是，为了恢复底层结构以及表面细节，给定部分输入，基本组件是一个良好的特征表示，可以捕获全局结构和局部几何细节。因此，我们首先提出了FSNet，一个特征结构化模块，可以自适应地聚合成一个2D结构化的特征地图逐点特征学习多个潜在的模式从局部区域。然后，我们将FSNet集成到一个粗到细的管道中，以完成点云。具体而言，采用2D卷积神经网络将FSNet中的特征图解码为粗糙且完整的点云。接下来，使用点云上采样网络从部分输入和粗略的中间输出生成密集点云。为了有效地利用局部结构并增强点分布的均匀性，我们提出了IFNet，这是一个具有自校正机制的点上采样模块，可以逐步细化生成的密集点云的细节。我们已经在ShapeNet，MVP和KITTI数据集上进行了定性和定量实验，这些实验表明我们的方法优于最先进的点云完成方法。
英文摘要：
In this paper, we tackle the challenging problem of point cloud completion from the perspective of feature learning. Our key observation is that to recover the underlying structures as well as surface details, given partial input, a fundamental component is a good feature representation that can capture both global structure and local geometric details. We accordingly first propose FSNet, a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local regions. We then integrate FSNet into a coarse-tofine pipeline for point cloud completion. Specifically, a 2D convolutional neural network is adopted to decode feature maps from FSNet into a coarse and complete point cloud. Next, a point cloud upsampling network is used to generate a dense point cloud from the partial input and the coarse intermediate output. To efficiently exploit local structures and enhance point distribution uniformity, we propose IFNet, a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point cloud. We have conducted qualitative and quantitative experiments on ShapeNet, MVP, and KITTI datasets, which demonstrate that our method outperforms state-of-theart point cloud completion approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
