Attention-guided self-supervised distinctive region detection in point clouds

Abstract Detecting distinctive regions in point clouds is a fundamental task in shape analysis, critical for applications such as fine-grained classification, shape retrieval, and shape matching. Recent unsupervised deep learning approaches have shown promise, moving beyond hand-crafted features and labeled data. However, their results as well as their specific distinctive point selection mechanisms leave room for improvement. This work aims to enhance these approaches and extend them to more general learning scenarios. We propose two key algorithmic improvements: first, an attention-based mechanism for selecting distinctive points, and second, a novel semantic consistency loss that enhances the framework’s ability to identify meaningful distinctive regions consistently within a given shape. Additionally, we extend the framework to a few-shot learning setup, useful in cases where distinctive regions are ambiguous or poorly defined. To support our research, we have constructed what we believe to be the first benchmark with ground-truth distinctive region labels. Our experimental results, conducted across multiple real and synthetic datasets, demonstrate that our approach, dubbed distinctive region attention-guided detection in point clouds (DRAG), provides significant improvements over state-of-the-art methods.