Segmentation of 3D Point Clouds of Heritage Buildings Using Edge Detection and Supervoxel-Based Topology.

This paper presents a novel segmentation algorithm specially developed for applications in 3D point clouds with high variability and noise, particularly suitable for heritage building 3D data. The method can be categorized within the segmentation procedures based on edge detection. In addition, it uses a graph-based topological structure generated from the supervoxelization of the 3D point clouds, which is used to make the closure of the edge points and to define the different segments. The algorithm provides a valuable tool for generating results that can be used in subsequent classification tasks and broader computer applications dealing with 3D point clouds. One of the characteristics of this segmentation method is that it is unsupervised, which makes it particularly advantageous for heritage applications where labelled data is scarce. It is also easily adaptable to different edge point detection and supervoxelization algorithms. Finally, the results show that the 3D data can be segmented into different architectural elements, which is important for further classification or recognition. Extensive testing on real data from historic buildings demonstrated the effectiveness of the method. The results show superior performance compared to three other segmentation methods, both globally and in the segmentation of planar and curved zones of historic buildings.

Geometric and colour data fusion for outdoor 3D models.

This paper deals with the generation of accurate, dense and coloured 3D models of outdoor scenarios from scanners. This is a challenging research field in which several problems still remain unsolved. In particular, the process of 3D model creation in outdoor scenes may be inefficient if the scene is digitalized under unsuitable technical (specific scanner on-board camera) and environmental (rain, dampness, changing illumination) conditions. We address our research towards the integration of images and range data to produce photorealistic models. Our proposal is based on decoupling the colour integration and geometry reconstruction stages, making them independent and controlled processes. This issue is approached from two different viewpoints. On the one hand, given a complete model (geometry plus texture), we propose a method to modify the original texture provided by the scanner on-board camera with the colour information extracted from external images taken at given moments and under specific environmental conditions. On the other hand, we propose an algorithm to directly assign external images onto the complete geometric model, thus avoiding tedious on-line calibration processes. We present the work conducted on two large Roman archaeological sites dating from the first century A.D., namely, the Theatre of Segobriga and the Fori Porticus of Emerita Augusta, both in Spain. The results obtained demonstrate that our approach could be useful in the digitalization and 3D modelling fields.

Restitution of sculptural groups using 3D scanners.

Imagine for a moment that you have to solve a 3D jigsaw of which you have lost several pieces. You have also lost the original box-top showing the final picture, and as if that were not enough, some of the pieces you do have may belong to some other jigsaw. This is in essence the sort of challenge that we faced in the novel project that we shall be describing in this paper. The final aim of the project was, with the help of 3D scanners, to digitalize and reconstruct multi-piece classical sculptures. Particularly, we tackle the restitution of the so-called "Aeneas Group", a famous iconographic reference during the Roman Empire. We have undertaken this ambitious project in collaboration with the research department of the Spanish National Museum of Roman Art (MNAR). This paper summarizes the real problems that arose and had to be solved, the innovations, and the main results of the work that we have carried out over these recent years.