The design of a tiny machine learning model, which can be deployed in mobile and edge devices, for point cloud object classification is investigated in this work. To achieve this objective, we replace the multi-scale representation of a point cloud object with a single-scale representation for complexity reduction, and exploit rich 3D geometric information of a point cloud object for performance improvement. The proposed solution is named Green-PointHop due to its low computational complexity. We evaluate the performance of Green-PointHop on ModelNet40 and ScanObjectNN two datasets. Green-PointHop has a model size of 64K parameters. It demands 2.3M floating-point operations (FLOPs) to classify a ModelNet40 object of 1024 down-sampled points. Its classification performance gaps against the state-of-the-art DGCNN method are 3% and 7% for ModelNet40 and ScanObjectNN, respectively. On the other hand, the model size and inference complexity of DGCNN are 42X and 1203X of those of Green-PointHop, respectively.

翻译：本文研究了一种可部署在移动和边缘设备上的微型机器学习模型的设计，用于点云物体分类。为实现这一目标，我们将点云物体的多尺度表示替换为单尺度表示以降低复杂度，并利用点云物体丰富的三维几何信息提升性能。所提出的解决方案因其低计算复杂度被命名为Green-PointHop。我们在ModelNet40和ScanObjectNN两个数据集上评估了Green-PointHop的性能。Green-PointHop的模型大小为64K参数，对1024个下采样点的ModelNet40物体进行分类需要230万次浮点运算（FLOPs）。与最先进的DGCNN方法相比，其在ModelNet40和ScanObjectNN上的分类性能差距分别为3%和7%。另一方面，DGCNN的模型大小和推理复杂度分别是Green-PointHop的42倍和1203倍。