Fractual Projection Forest: Fast and Explainable Point Cloud Classifier

Hanxiao Tan; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 4240-4249

Point clouds are playing an increasingly important roll in autonomous driving and robotics. Although current point cloud classification models have achieved satisfactory accuracies, most of them trade slight performance gains by stacking complex modules on the grouping-local-global framework, which leads to prolonged processing time and deteriorating interpretability. In this work, we propose a new pipeline named Fractual Projection Forest (FPF) that exploits fractal features to enable traditional machine learning models to achieve competitive performance with DNNs on classification tasks. Though compromises by few percentages in accuracy compared to DNNs, FPF is faster, more interpretable, and easily extendable. We hope that FPF may provide the community with a novel view of point cloud classification. Our code is available on https://github.com/Explain3D/FracProjForest.