Cave feature extraction and classification from rockery point clouds acquired with handheld laser scanners

Abstract

Rockeries are unique elements of Chinese classical gardens with historical, cultural, artistic, and scientific value. One of the essential characteristics of garden rockery is the cave features since cave morphological features determine the degree of “Tou” (riddled through) and “Lou” (hollowed out well) features as the defined by rockery appreciation theory, which is important for rockery heritage conservation, assessment, and management. However, in heritage studies, accurately identifying and evaluating rockery caves is a difficult task because of a cave's irregular shape. This paper proposes a methodology to extract and classify cave features by point clouds obtained from data using a handheld laser scanner with a camera. Without completing surface reconstruction, the rockery point cloud is first sliced into chips, then cave chips were extracted from these approximately two-dimensional chips and next merged to obtain three-dimensional cave point clouds. Finally, the cave boundary points are extracted from the cave and fitted by an ellipse for classification. To extract and classify cave features, a methodology to improve rockery digitalization quality is also proposed. The raw point cloud data were preprocessed by pose adjustment, noise removal, and hole repair. The experimental results for the two rockeries in Tongji University and Qiuxiapu Garden indicate that the improved digitization scheme generates complete and closed rockery point clouds, all types of caves were effectively extracted and classified by our proposed method. Additionally, the extracted caves are still represented by point clouds, which suggest the possibility for other research in the future.