A Planar Feature-Preserving Texture Defragmentation Method for 3D Urban Building Models
Oblique photogrammetry-based 3D modeling is widely used for large-scale urban reconstruction. However, textures generated with photogrammetric techniques often exhibit scattered and irregular characteristics, which lead to significant challenges with texture seams and UV map discontinuities, increasing storage requirements and affecting rendering quality. In this paper, we propose a planar feature-preserving texture defragmentation method designed specifically for urban building models. Our approach leverages the multi-planar topology of buildings to optimize texture merging and reduce fragmentation. The proposed approach is composed of three main stages: the extraction of planar features from texture fragments; the employment of these planar elements as guiding constraints for merging adjacent texture fragments within a two-dimensional texture space; and an enhanced texture-packing algorithm designed for more regular texture charts to systematically generate refined texture atlas. Experiments on various urban building models demonstrate that our method significantly improves texture continuity and storage efficiency compared to traditional approaches, with both quantitative and qualitative validation.
Authors
- Tarek M. Awwad (ORCID: https://orcid.org/0000-0003-4687-3678)
- Xianfeng Huang (ORCID: https://orcid.org/0000-0002-2835-5810)
- Fan Zhang (ORCID: https://orcid.org/0000-0001-9476-9862)
- Wenxuan Liu (ORCID: https://orcid.org/0000-0002-1528-4224)
- Beining Liu
- Zhen Lei (ORCID: https://orcid.org/0000-0002-9813-479X)
Institutions
- Northern Border University (SA)
- Ministry of Natural Resources (CN)
- Wuhan University (CN)
- State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN)
Publication Details
- Journal
- Remote Sensing
- Published
- 2024-11-07
- DOI
- https://doi.org/10.3390/rs16224154
- Citations
- 1
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.21