Reconstructing a high-resolution 3D model of an object is a challenging task
in computer vision. Designing scalable and light-weight architectures is
crucial while addressing this problem. Existing point-cloud based
reconstruction approaches directly predict the entire point cloud in a single
stage. Although this technique can handle low-resolution point clouds, it is
not a viable solution for generating dense, high-resolution outputs. In this
work, we introduce DensePCR, a deep pyramidal network for point cloud
reconstruction that hierarchically predicts point clouds of increasing
resolution. Towards this end, we propose an architecture that first predicts a
low-resolution point cloud, and then hierarchically increases the resolution by
aggregating local and global point features to deform a grid. Our method
generates point clouds that are accurate, uniform and dense. Through extensive
quantitative and qualitative evaluation on synthetic and real datasets, we
demonstrate that DensePCR outperforms the existing state-of-the-art point cloud
reconstruction works, while also providing a light-weight and scalable
architecture for predicting high-resolution outputs.

本文设计了一个基于深度金字塔网络的点云重建方法 DensePCR，通过逐层生成低分辨率到高分辨率的点云来生成准确、均匀且密集的高分辨率 3D 模型。在合成和真实数据集上的评估表明，DensePCR 优于现有的点云重建方法，并提供了一种轻量级和可扩展的高分辨率输出预测体系结构。