Dense-Resolution Network for Point Cloud Classification and Segmentation
This work addresses point cloud classification and segmentation for applications such as robotics and self-driving, representing an incremental improvement over existing methods.
The authors tackled the challenge of point cloud analysis by proposing Dense-Resolution Network (DRNet), which learns local features at different resolutions and achieved superior performance on benchmarks like ModelNet40, ShapeNet, and ScanObjectNN.
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.