Fast adaptive multimodal feature registration (FAMFR): an effective high-resolution point clouds registration workflow for cultural heritage interiors

Abstract

Accurate registration of 3D scans is crucial in creating precise and detailed 3D models for various applications in cultural heritage. The dataset used in this study comprised numerous point clouds collected from different rooms in the Museum of King Jan III’s Palace in Warsaw using a structured light scanner. Point clouds from three relatively small rooms at Wilanow Palace: The King’s Chinese Cabinet, The King’s Wardrobe, and The Queen’s Antecabinet exhibit intricate geometric and decorative surfaces with diverse colour and reflective properties. As a result, creating a high-resolution full 3D model require a complex and time-consuming registration process. This process often consists of several steps: data preparation, registering point clouds, final relaxation, and evaluation of the resulting model. Registering two-point clouds is the most fundamental part of this process; therefore, an effective registration workflow capable of precisely registering two-point clouds representing various cultural heritage interiors is proposed in this paper. Fast Adaptive Multimodal Feature Registration (FAMFR) workflow is based on two different handcrafted features, utilising the colour and shape of the object to accurately register point clouds with extensive surface geometry details or geometrically deficient but with rich colour decorations. Furthermore, this work emphasises the challenges associated with high-resolution point clouds registration, providing an overview of various registration techniques ranging from feature-based classic approaches to new ones based on deep learning. A comparison shows that the algorithm explicitly created for this data achieved much better results than traditional feature-based or deep learning methods by at least 35%.