arXiv:2512.02496v1 Announce Type: new 
Abstract: This study investigates the impact of the invariance of feature vectors for partial-to-partial point set registration under translation and rotation of input point sets, particularly in the realm of techniques based on deep learning and Gaussian mixture models (GMMs). We reveal both theoretical and practical problems associated with such deep-learning-based registration methods using GMMs, with a particular focus on the limitations of DeepGMR, a pioneering study in this line, to the partial-to-partial point set registration. Our primary goal is to uncover the causes behind such methods and propose a comprehensible solution for that. To address this, we introduce an attention-based reference point shifting (ARPS) layer, which robustly identifies a common reference point of two partial point sets, thereby acquiring transformation-invariant features. The ARPS layer employs a well-studied attention module to find a common reference point rather than the overlap region. Owing to this, it significantly enhances the performance of DeepGMR and its recent variant, UGMMReg. Furthermore, these extension models outperform even prior deep learning methods using attention blocks and Transformer to extract the overlap region or common reference points. We believe these findings provide deeper insights into registration methods using deep learning and GMMs.

تم نشر دراسة جديدة تبحث في عدم تغير متجهات الميزات لتسجيل مجموعات النقاط الجزئية، مع التركيز بشكل خاص على تقنيات التعلم العميق ونماذج المزيج الغاوسي (GMM). تسلط الدراسة الضوء على قيود الأساليب الحالية، مثل DeepGMR، وتقدم طبقة نقل نقطة مرجعية موجهة بالانتباه (ARPS) تهدف إلى تحسين تحديد النقاط المرجعية المشتركة في مجموعات النقاط الجزئية.

Se ha publicado un nuevo estudio que investiga la invariancia de los vectores de características para el registro de conjuntos de puntos parciales, centrándose especialmente en técnicas de aprendizaje profundo y modelos de mezcla gaussiana (GMM). La investigación destaca las limitaciones de los métodos existentes, como DeepGMR, e introduce una capa de desplazamiento de punto de referencia guiada por atención (ARPS) destinada a mejorar la identificación de puntos de referencia comunes en conjuntos de puntos parciales.

Une nouvelle étude a été publiée, examinant l'invariance des vecteurs de caractéristiques pour l'enregistrement de jeux de points partiels, en se concentrant particulièrement sur les techniques d'apprentissage profond et les modèles de mélange gaussien (GMM). La recherche met en évidence les limitations des méthodes existantes, telles que DeepGMR, et introduit une couche de décalage de point de référence guidée par l'attention (ARPS) visant à améliorer l'identification des points de référence communs dans les ensembles de points partiels.

A new study has been published investigating the invariance of feature vectors for partial-to-partial point set registration, particularly focusing on deep learning techniques and Gaussian mixture models (GMMs). The research highlights the limitations of existing methods, such as DeepGMR, and introduces an attention-based reference point shifting (ARPS) layer aimed at improving the identification of common reference points in partial point sets.

Attention-guided reference point shifting for Gaussian-mixture-based partial point set registration

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Attention-guided reference point shifting for Gaussian-mixture-based partial point set registration

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