3D Shape Classification by Registration: Neural-Network-Free and Training-Free

Point cloud classification, crucial for discriminative 3D shape analysis, has witnessed significant progress through the application of deep learning. A significant research focus has been on aggregating local point cloud features. A key limitation of previous methods lies in their inherent opacity, making it challenging to understand the underlying reasons for their predictions. Furthermore, these methods frequently exhibit poor generalization performance, struggling to maintain their efficacy when applied to data that deviates from their training distribution. Our method aims to tackle these challenges. We propose ICP-Classifier, a neural-network-free and training-free paradigm that uses Iterative Closest Point (ICP) for classification, which is simple yet robust. The inherent transparency of our method allows for straightforward interpretation of its predictions and underlying mechanisms. By comparing test samples with a reference library, ICP-Classifier predicts labels based on overlap ratios, showing strong generalization on out-of-distribution datasets and robustness facing perturbed data. While capable of achieving high accuracy, our work is exploratory in nature, aiming to explore potential solutions for existing challenges. In contrast to the current focus on complex local feature aggregation, our findings suggest that the inherent shape of 3D objects holds significant discriminative potential, opening up new avenues for exploring robust methods and deepening our understanding of 3D shape analysis.