单视图和多视图深度估计的不确定性受约束的融合,用于AR虚拟现实阻塞
Jia Liu1, Shuai Ding1, Yongze Li1
1School of Automation, C-IMER, CICAEET, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
概括
本研究引入了一种用于增强现实 (AR) 的新型混合深度估计算法,以改善虚拟现实封闭. 该方法在复杂场景中提高了深度准确性,从而导致更现实的AR交互.
科学领域:
- 计算机视觉 计算机视觉
- 增强现实是一种增强现实.
- 计算机图形 计算机图形
背景情况:
- 虚拟现实封闭是增强现实 (AR) 中的一个关键挑战,影响现实主义和沉浸.
- 基于深度的方法提供了实时AR潜力,但需要准确的深度估计,这在复杂的场景中很困难.
- 现有的深度估计算法在交互式和复杂的环境中与性能退化作斗争.
研究的目的:
- 为复杂的AR环境设计一个强大的深度估计算法.
- 为了提高虚拟对象和物理世界之间的遮处理的准确性和稳定性,在AR.
- 在增强现实应用中增强用户沉浸感和交互现实性.
主要方法:
- 开发了一个混合深度估计算法,集成单视图和多视图线索.
- 该模型包含一个单视图编码器来增加多视图成本量和一个基于贝叶斯卷积的不确定性模块.
- 由单视图分支生成的动态面罩减轻了场景动态对深度估计的影响.
主要成果:
- 与ScanNet v2.2上的SimpleRecon基线相比,提出的方法实现了AbsRel的23.9%降低和RMSE的28%降低.
- 精度指标 (δ 1) 提高了5.43个百分点,表明深度预测可靠性得到了提高.
- 边界交叉点在欧盟 (IoU) 的AR阻塞评估增加了7.13%,显示出更清晰,更可靠的阻塞处理.
结论:
- 混合深度估计算法有效地解决了复杂AR场景中的虚拟现实封闭挑战.
- 整合单视图和多视图线索,以及消除不确定性,显著提高了深度估计的准确性.
- 该方法提供了更可靠的深度数据和更清晰的阻塞处理,推进了增强现实体验的现实性.
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