Research in 3D semantic segmentation has been increasing performance metrics, like the IoU, by scaling model complexity and computational resources, leaving behind researchers and practitioners that (1) cannot access the necessary resources and (2) do need transparency on the model decision mechanisms. In this paper, we propose SCENE-Net, a low-resource white-box model for 3D point cloud semantic segmentation. SCENE-Net identifies signature shapes on the point cloud via group equivariant non-expansive operators (GENEOs), providing intrinsic geometric interpretability. Our training time on a laptop is 85~min, and our inference time is 20~ms. SCENE-Net has 11 trainable geometrical parameters and requires fewer data than black-box models. SCENE--Net offers robustness to noisy labeling and data imbalance and has comparable IoU to state-of-the-art methods. With this paper, we release a 40~000 Km labeled dataset of rural terrain point clouds and our code implementation.

翻译：3D语义分割研究通过扩展模型复杂度和计算资源持续提升交并比（IoU）等性能指标，但这使得（1）无法获取必要资源且（2）需要模型决策机制透明化的研究人员和实践者被边缘化。本文提出SCENE-Net——一种用于3D点云语义分割的低资源白盒模型。该模型通过群等变非扩张算子（GENEOs）识别点云中的签名形状，具有内在的几何可解释性。其笔记本电脑端训练时间为85分钟，推理时间为20毫秒。SCENE-Net仅含11个可训练几何参数，且所需数据量少于黑盒模型。该模型对噪声标注和数据不平衡具有鲁棒性，其IoU性能与前沿方法相当。本文还发布了包含40,000公里的乡村地形点云标注数据集及代码实现。