Efficient Point Cloud Object Classifications with GhostMLP

Abstract

Efficient models capable of handling large numbers of data points in point cloud research are in high demand in computer vision. Despite recent advancements in 3D classification and segmentation tasks in point cloud processing, the deep learning PointNeXt and PointMLP models are plagued with heavy computation requirements with limited efficiencies. In this paper, a novel GhostMLP model for point clouds is thus introduced. It takes the advantages of the GhostNet design modules and uses them to replace the MLP layers in the existing PointMLP model. The resulting GhostMLP architecture achieves superior classification performance with lower computation requirements. Compared to the PointMLP, GhostMLP maintains sustainable performance with fewer parameters and lower FLOPs computations. Indeed, it outperforms PointMLP on the ScanObjectNN dataset, achieving 88.7% overall accuracy and 87.6% mean accuracy with only 6 million parameters and 7.2 GFLOPs—about half the resources required by PointMLP. At the same time, GhostMLP-S is introduced as a lightweight version which also outperforms PointMLP in performance. GhostMLP completes faster training and inference with GPU and is the best-performing method that does not require any extra training data in the ScanObjectNN benchmark. Efficient point cloud analysis is essential in computer vision, and we believe that GhostMLP has the potential to become a powerful tool for large-scale point cloud analysis.