Abstract
In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented.
Highlights
Semantic segmentation is one of the most important research methods for computer vision, and has the task to classify each pixel or point in the scene into classes that have specific features [1,2]
The results of deep learning (DL) are satisfactory, as they demonstrate the achievement of Overall Accuracy (OA) similar to those of Random Forest (RF), the training set is partially limited, if compared to the others present in the state of the art
This study explored semantic segmentation of complex 3D point clouds in the cultural heritage (CH) domain
Summary
Semantic segmentation is one of the most important research methods for computer vision, and has the task to classify each pixel or point in the scene into classes that have specific features [1,2]. Semantic segmentation concerned bi-dimensional images but, due to some limitations related to occlusions, illumination, posture and other problems, the researches began to deal with three-dimensional data. This change occurred thanks to the growing diffusion of photogrammetry and laser scanning surveys. The automatic interpretation of 3D point clouds by semantic segmentation in the cultural heritage (CH) context represents a very challenging task. Notwithstanding, the understanding of 3D scenes in digital CH is crucial, as it can have many applications such as the identification of similar architectural elements in large dataset, the analysis of the state of conservation of materials, the subdivision of the point clouds in its structural parts preliminary for scan-to-BIM processes, etc. Notwithstanding, the understanding of 3D scenes in digital CH is crucial, as it can have many applications such as the identification of similar architectural elements in large dataset, the analysis of the state of conservation of materials, the subdivision of the point clouds in its structural parts preliminary for scan-to-BIM processes, etc. [5]
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