亚特兰大-世界内部的深层场景合成从单一的全向图像
IEEE transactions on visualization and computer graphics
|October 2, 2023
概括
本研究介绍了一种深度学习方法,可以从单个全景图像中提取3D空间几何. 这使得增强的虚拟现实 (VR) 经验能够通过以交互速度生成新的视图.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 虚拟现实 虚拟现实 虚拟现实
背景情况:
- 从单个图像中估计3D几何是具有挑战性的,因为固有的模两可.
- 虚拟现实 (VR) 应用需要沉浸式3D环境和新的视图合成.
研究的目的:
- 开发一种数据驱动的方法,从室内场景的单一全景图像中提取几何和结构信息.
- 通过从新的视角呈现场景来增强VR应用中的3D沉浸感.
主要方法:
- 一种新的端到端深度学习方法共同估计了深度和房间结构,利用了"亚特兰大世界"的先验 (水平地板/天花板,垂直墙壁).
- 该方法利用域特定的损失函数和对合成全景数据的广泛培训.
- 一个轻量级的网络从翻译的位置推断出新的全景,使透视图生成和升级采样的交互速率成为可能.
主要成果:
- 这种方法自动在原始摄像机周围以交互速度生成新的姿势.
- 它产生适合VR应用的深度线索,特别是用于头部显示器.
- 提取的地图和3D墙体结构支持房间的探索.
结论:
- 该方法实现了低延迟性能,并提高了预测准确度,相比于室内全景基准的最先进解决方案.
- 它有效地解决了单图像几何估计和VR的新视图合成挑战.
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