面具到骨:基于多视图面具的树骨提取与3D高斯斯喷涂.
Xinpeng Liu1, Kanyu Xu1, Risa Shinoda1
1Graduate School of Information Science and Technology, The University of Osaka, 1-5 Yamadaoka, Suita 565-0871, Osaka, Japan.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
本研究介绍了一种新的面具引导图形优化框架,用于3D树骨架重建. 它绕过了杂的3D点云,提高了植物建模的准确性和完整性.
科学领域:
- 计算机视觉 计算机视觉
- 计算几何学的计算几何学
- 植物科学 植物科学
背景情况:
- 从多视图图像中重建3D树是很困难的.
- 使用3D点云的现有方法与细分枝和低质感对比度扎,导致数据碎片化和连接断裂.
研究的目的:
- 开发一种可靠的方法来准确的3D树骨架重建.
- 通过利用图像分割面具来克服基于点云的方法的局限性.
主要方法:
- 提出了一个面具引导图形优化框架.
- 3D骨架是以图形形式建模的,使用3D高斯点击 (3DGS) 进行了优化,用于轮染,并使用最小跨度树 (MST) 算法进行规范化.
主要成果:
- 这种新方法直接从多视图细分面具中估计3D骨架.
- 与现有方法相比,在完整性和结构准确性方面表现出持续的改进.
结论:
- 面具引导图形优化框架为3D树骨架重建提供了更可靠的方法.
- 这种方法提高了结构的准确性和完整性,特别是在具有挑战性的植物结构中.
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