C2P-Net:用于房间布局估计的全面深度地图到平面深度转换.
概括
本研究引入了一种使用全面深度图和平面可见度信心来估计房间布局的新方法. 该方法改进了从图像中推断室内空间配置,优于现有技术.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 房间布局估计对于从图像中理解室内空间配置至关重要.
- 在重建室内飞机时,现有的方法在与遮和位置依赖性作斗争.
- 对主导室内平面的准确重建对于布局估计至关重要.
研究的目的:
- 开发一种可靠的房间布局估计方法,适用于全景图像和视角图像.
- 为了克服现有的像素级或实例级平面参数学习的局限性.
- 为改进平面深度重建引入全新全面深度地图到平面深度 (C2P) 转换.
主要方法:
- 介绍了全面深度图到平面深度 (C2P) 转换技术.
- 开发了一个框架,共同学习一个全面的深度地图和平面可见性信心.
- 通过连续平面深度地图集成提出了一种用于3D布局生成的新方法.
主要成果:
- 在C2P转换证明了适用于全景和视角图像.
- 拟议的网络通过可分化的C2P转换自主学习平面可见度信心.
- 实验结果显示,在所有评估的全景和视角数据集中,性能优越.
结论:
- 开发的方法在房间布局估计方面提供了显著的进步.
- C2P转换为平面深度重建提供了更强大的方法.
- 该框架能够从图像数据中生成准确的3D布局.
相关概念视频
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