大规模的室内视觉-几何多式数据集和新视图合成的基准
Junming Cao1,2, Xiting Zhao3, Sören Schwertfeger3
1Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
我们介绍了一个大规模的室内数据集和基准,以改进用于增强现实,虚拟现实和机器人的3D场景重建. 本资源解决了现有数据集的局限性,使得新的视图合成 (NVS) 技术更为强大.
科学领域:
- 计算机视觉 计算机视觉
- 3D重建的3D重建
- 机器人技术 机器人技术 机器人技术
背景情况:
- 准确的室内环境重建对于AR,VR和机器人技术至关重要.
- 现有的数据集缺乏规模,地面真相点云,以及足够的观点来进行强大的新视图合成 (NVS).
研究的目的:
- 引入一个大规模的室内数据集和基准来评估NVS算法.
- 解决当前数据集的局限性,以推进室内场景重建.
主要方法:
- 收集了各种室内场景的全景图像序列,高分辨率点云,网格和纹理.
- 开发了一种针对复杂的室内环境而定制的新基准.
主要成果:
- 该数据集具有具有挑战性的场景,如地下室和长走廊.
- 为NVS技术的严格评估提供了全面的基础真相数据.
结论:
- 新的数据集和基准将促进开发更有效的NVS解决方案.
- 旨在推进室内场景重建领域的现实应用.
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