  Point cloud analysis is attracting attention from Artificial Intelligence
research since it can be widely used in applications such as robotics,
Augmented Reality, self-driving. However, it is always challenging due to
irregularities, unorderedness, and sparsity. In this article, we propose a
novel network named Dense-Resolution Network (DRNet) for point cloud analysis.
Our DRNet is designed to learn local point features from the point cloud in
different resolutions. In order to learn local point groups more effectively,
we present a novel grouping method for local neighborhood searching and an
error-minimizing module for capturing local features. In addition to validating
the network on widely used point cloud segmentation and classification
benchmarks, we also test and visualize the performance of the components.
Comparing with other state-of-the-art methods, our network shows superiority on
ModelNet40, ShapeNet synthetic and ScanObjectNN real point cloud datasets.
