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.

本文提出了一种低资源的白盒子模型 SCENE-Net，利用类群不变非扩张算子（GENEOs）在点云中识别签名形状，提供固有几何可解释性，在保证模型决策机制透明性的同时，使训练时间在 85 分钟、推理时间在 20ms，需要的可训练几何参数仅 11 个，数据要求较黑盒子模型更少。SCENE-Net 对噪声标签和数据不平衡具有鲁棒性，与最先进的方法具有可比的 IoU。同时，本文公布了 40000 公里的农村地形点云标记数据集和代码实现。