论文标题
单眼视频中虚拟物体的刻度意识插入
Scale-aware Insertion of Virtual Objects in Monocular Videos
论文作者
论文摘要
在本文中，我们提出了一种尺度感知方法，用于将其适当大小的虚拟对象插入单眼视频中。为了解决从单眼视频中恢复几何形状问题的规模歧义问题，我们估计具有贝叶斯方法的视频中的全球尺度对象，其中包含对象的大小先验，其中场景对象的大小应严格符合相同的全球尺度，并且根据对象类别的尺寸分布，最大化了全球尺度的可能性。为此，我们提出了一个大小的对象类别的数据集：度量树，带有相应图像的900多个对象类别大小的层次表示。为了处理从视频中恢复的对象的不完整性，我们提出了一种新颖的规模估计方法，该方法提取物体的合理维度以进行比例优化。实验表明，我们的规模估计方法的性能优于最先进的方法，并且对于不同的视频场景具有相当大的有效性和鲁棒性。公制的树已在以下网址提供：https：//metric-tree.github.io
In this paper, we propose a scale-aware method for inserting virtual objects with proper sizes into monocular videos. To tackle the scale ambiguity problem of geometry recovery from monocular videos, we estimate the global scale objects in a video with a Bayesian approach incorporating the size priors of objects, where the scene objects sizes should strictly conform to the same global scale and the possibilities of global scales are maximized according to the size distribution of object categories. To do so, we propose a dataset of sizes of object categories: Metric-Tree, a hierarchical representation of sizes of more than 900 object categories with the corresponding images. To handle the incompleteness of objects recovered from videos, we propose a novel scale estimation method that extracts plausible dimensions of objects for scale optimization. Experiments have shown that our method for scale estimation performs better than the state-of-the-art methods, and has considerable validity and robustness for different video scenes. Metric-Tree has been made available at: https://metric-tree.github.io
