学习光度特征转换为自由形式的对象扫描.
IEEE transactions on visualization and computer graphics
|March 3, 2025
概括
这项研究引入了3D重建的新框架,通过学习将光度测量转化为视图不变特征来提高准确性. 该系统有效地从手持扫描中重建几何和反射率.
科学领域:
- 计算机视觉 计算机视觉
- 三维重建的3D重建
- 摄像度立体声 摄像度立体声
背景情况:
- 多视图立体声 (MVS) 管道经常与不同的照明和非结构化数据作斗争.
- 提取强大,视图不变的特征对于准确的3D形状和反射率重建至关重要.
研究的目的:
- 开发一种新的框架,用于自动学习聚合和转换光度测量.
- 通过生成空间上独特的,视图不变的低层特征来提高3D重建质量.
- 从具有挑战性的手持扫描中重建几何和异性反射.
主要方法:
- 一个新的框架共同训练合成数据上的照明条件和特征转换.
- 功能被输入到多视图立体管道中,以改善3D重建.
- 开发了一个系统来从手持扫描中重建几何和异性反射率.
主要成果:
- 该系统使用轻型原型 (摄像头,LED,平板电脑) 证明了它的有效性.
- 重建与专业的3D扫描仪和照片进行验证.
- 拟议的方法与3D重建的最先进技术相比较有利.
结论:
- 该框架成功地生成了视图不变的功能,用于增强的3D重建.
- 该系统能够从具有挑战性的数据集中对几何和反射率进行可靠的重建.
- 这种方法比现有的多视图立体声技术有了显著的改进.
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