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%.

Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures

“…The detected points and matched point pairs in both reference and slave image using SURF feature detectors are shown in Figure 9. Another image-registration method is known as feature-based registration, which relies on identifying and matching distinctive features or key points in the images [127][128][129]. Point-based registration using algorithms like Scale-Invariant Feature Transform (SIFT) or Speeded-Up Robust Features (SURF) is common.…”

“…Thresholding is particularly useful for applications where structural components exhibit distinct intensity differences, enabling the separation of different regions of interest. Another widely used method is edge-based segmentation, with the Another image-registration method is known as feature-based registration, which relies on identifying and matching distinctive features or key points in the images [127][128][129]. Point-based registration using algorithms like Scale-Invariant Feature Transform (SIFT) or Speeded-Up Robust Features (SURF) is common.…”

Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System

“…Although these studies did not validate their methods with tangible stop-and-go scanning systems, they showcased their significant potential for remedying the auto-registration challenges inherent in stop-and-go scanning applications. Foryś et al [ 66 ] introduced the FAMFR algorithm to precisely register two-point clouds representing various cultural heritage interiors based on two different handcrafted features, utilizing the color and shape of the object to accurately register point clouds with extensive surface geometry details or geometrically deficient but with rich color decorations.…”