Exploring Point Transformers on 3D Semantic Segmentation of Javanese Architectures
Abstract
Abstract. The complex geometry of Javanese architecture poses significant challenges for 3D semantic segmentation in cultural heritage documentation. This study evaluates state-of-the-art Point Transformers, i.e., PTv1, PTv2, PTv3, and LitePT, on the Sewu temple dataset, focusing on robustness and efficiency. While PTv1 and PTv2 achieve the highest Intersection-over-Union (mIoU 0.71), they incur high computational costs. Conversely, LitePT provides an optimal balance, delivering competitive results (0.69 mIoU) while being drastically faster. Furthermore, experiments with limited data reveal the significant benefits of transfer learning from European heritage datasets. We conclude that efficient Point Transformer architectures are promising for the automated understanding of complex non-European monuments.
Fields of Study & Subjects
3D Surveying and Cultural Heritage
3D Shape Modeling and Analysis
Advanced Neural Network Applications
Publication details
- Journal / source
- The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
- Volume
- XLVIII-2/W12-2026
- Pages
- 57–64
- Publication date
- 12 February 2026
- Language
- EN