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通过多任务学习和反向染进行室内全景图像的虚拟分期
IEEE computer graphics and applications
|September 3, 2025
概括
室内场景的虚拟演出现在可以与VISPI (单个室内全景图像的虚拟演出管道) 进行交互. 这个框架使用深度学习和实时染来实现单个360度图像的真实虚拟分期.
科学领域:
- 计算机视觉
- 计算机图形
- 人工智能
背景情况:
- 360°室内图像提供了具有成本效益的沉浸式内容创作.
- 虚拟设置,包括物体移除和现实的插入照明,带来了重大技术挑战.
研究的目的:
- 推出VISPI (单个室内全景图像虚拟设置管道),这是互动虚拟设置的新框架.
- 通过单一的全景图像来操纵室内场景.
主要方法:
- 使用视觉转换器提取几何,语义和材料属性的多任务深度学习方法.
- 集成球形高斯照明估计和实时染以实现交互式物体放置.
- 开发一种双通道系统,用于无杂乱的场景表示和材料属性估计 (金属,粗性).
主要成果:
- 从杂乱的室内场景中同时预测深度,正常值,语义,白度和材料属性.
- 成功生成立体多中心投影视图,用于头部显示器的探索.
- 结构3D和FutureHouse数据集框架的有效性.
结论:
- 使用单一全景图像进行互动虚拟演出.
- 这种框架可以帮助创建和编辑现实的虚拟环境.
- 应用包括增强的房地产可视化,室内设计和沉浸式虚拟体验.
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