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Stable Realization of Virtual Reality by Model Selection

通过模型选择稳定实现虚拟现实

基本信息

批准号：
11680377
负责人：
KANATANI Kenichi
金额：
$ 2.3万
依托单位：
GUNMA UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1999
资助国家：
日本
起止时间：
1999 至 2000
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-11680377/
关键词：
3-D reconstruction geometric model selection statistical optimization model selection criterion virtual studio moving camera calibration image mosaicing optical flow モザイク生成モデル選択幾何学的AIC 動画像解析 3次元復元信頼性評価

项目摘要

1.3-D reconstruction from imagesWe have presented a theory for computing the 3-D structure of the scene and objects from images with maximum possible accuracy and evaluating the reliability of the computed solution in quantitative terms. We have demonstrated the effectiveness of our theory by applying it to 3-D reconstruction from two images and 3-D interpretation of optical flow.We have also shown how to compute the covariance matrices that characterize the accuracy of feature points extracted from images and experimentally confirmed their positive correlation with their reliability evaluation. We have further analyzed the effects of normalization of the absolute position and the scale for describing the reliability of the computed shape. A new technique for displaying curves and surfaces are also proposed.We have thus given a theoretical foundation to the reliability evaluation of 3-D reconstruction from images.2.Virtual reality realization by geometric model selectionyWe have derived the "geometric AIC" and the "geometric MDL" as model selection criteria for geometric inference of noisy data extracted from images and given an information-theoretic interpretation. We have applied these criteria to image mosaicing and demonstrated that the image transformation can be stably computed even if the overlaps between captured images are small.We have also designed an optimal reference pattern for calibrating a moving camera for virtual studio applications and confirmed by simulations and real-image experiments that the degeneracy of camera configuration and statistical fluctuations of the solution can be avoided by incorporating geometric model selection. Furthermore, we have demonstrated by simulations and real-image experiments that the accuracy of separating independent object motions and estimating the number of objects from their motion images can be dramatically improved by introducing geometric model selection.

从图像中重建1.3维我们提出了一种理论，用于以最大可能的精度从图像中计算场景和物体的三维结构，并以定量的方式评估计算结果的可靠性。通过将该理论应用于两幅图像的三维重建和光流的三维解释，验证了该理论的有效性。我们还介绍了如何计算表征从图像中提取的特征点的准确性的协方差矩阵，并通过实验证实了它们与其可靠性评估的正相关性。进一步分析了绝对位置归一化和尺度归一化对计算形状可靠性的影响。提出了一种新的曲线曲面显示技术，为图像三维重建的可靠性评价提供了理论基础。2.几何模型选择实现虚拟现实我们推导出了图像噪声数据几何推理的模型选择准则“几何AIC”和“几何MDL”，并给出了信息论解释。我们将这些准则应用到图像拼接中，证明了即使采集到的图像之间的重叠很小，图像变换也可以稳定地计算出来。我们还设计了一种用于虚拟演播室应用的运动摄像机标定的最佳参考模式，并通过模拟和真实图像实验证实，通过结合几何模型选择可以避免摄像机配置的退化和解的统计波动。此外，我们通过仿真和真实图像实验证明，通过引入几何模型选择，可以显著提高从运动图像中分离独立目标运动和估计目标数目的精度。