In this paper, we propose an uncertainty-aware multi-resolution learning for point cloud segmentation, named PointRas. Most existing works for point cloud segmentation design encoder networks to obtain better representation of local space in point cloud. However, few of them investigate the utilization of features in the lower resolutions produced by encoders and consider the contextual learning between various resolutions in decoder network. To address this, we propose to utilize the descriptive characteristic of point clouds in the lower resolutions. Taking reference to core steps of rasterization in 2D graphics where the properties of pixels in high density are interpolated from a few primitive shapes in rasterization rendering, we use the similar strategy where prediction maps in lower resolution are iteratively regressed and upsampled into higher resolutions. Moreover, to remedy the potential information deficiency of lower-resolution point cloud, we refine the predictions in each resolution under the criterion of uncertainty selection, which notably enhances the representation ability of the point cloud in lower resolutions. Our proposed PointRas module can be incorporated into the backbones of various point cloud segmentation frameworks, and brings only marginal computational cost. We evaluate the proposed method on challenging datasets including ScanNet, S3DIS, NPM3D, STPLS3D and ScanObjectNN, and consistently improve the performance in comparison with the state-of-the-art methods.

中文翻译：

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PointRas：点云分割的不确定性感知多分辨率学习


在本文中，我们提出了一种用于点云分割的不确定性感知多分辨率学习，称为PointRas。大多数现有的点云分割工作都设计编码器网络，以获得点云中局部空间的更好表示。然而，很少有人研究编码器产生的较低分辨率特征的利用，并考虑解码器网络中不同分辨率之间的上下文学习。为了解决这个问题，我们建议利用较低分辨率下点云的描述特征。参考 2D 图形中光栅化的核心步骤，其中高密度像素的属性是从光栅化渲染中的一些原始形状插值出来的，我们使用类似的策略，将较低分辨率的预测图迭代回归并上采样到更高分辨率。此外，为了弥补低分辨率点云潜在的信息不足，我们在不确定性选择的标准下细化每个分辨率的预测，这显着增强了点云在较低分辨率下的表示能力。我们提出的 PointRas 模块可以合并到各种点云分割框架的主干中，并且只带来边际计算成本。我们在具有挑战性的数据集（包括 ScanNet、S3DIS、NPM3D、STPLS3D 和 ScanObjectNN）上评估所提出的方法，并与最先进的方法相比，不断提高性能。