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3 Dimensional Shape Recovery from Image Sequences by Non-Linear Optimization

通过非线性优化从图像序列恢复 3 维形状

基本信息

批准号：
16500118
负责人：
ASADA Naoki
金额：
$ 2.18万
依托单位：
Hiroshima City University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2006
项目状态：
已结题

项目摘要

We have investigated a method for recovering 3 dimensional shapes from image sequence by using non-linear optimization.(1) Improvement of precision for detecting and tracking feature pointsWe have developed a method for detecting and tracking feature points under the affine and depth geometric constraints from the image sequences. First, the affine transform is applied to a pair of images including motion disparity, and then the corresponding points between the images are detected under the affine geometric constraint. After recovering the initial 3D shape, the depth geometric constraint is applied to remove the outlier points and to detect new corresponding points. After several iterations of this procedure, 3D model with 11,277 points was recovered from images of 25 frames in a simulation study, and that with 15,751 points was from 25 frames using real images. These experimental results have shown the effectiveness of our method to recover the 3D dense and precise models from image sequences.(2) Improvement of stability for computing non-linear optimization3D recovery of object shape and camera motion from 2D image sequence is formulated as a non-linear optimization problem, and the initial values are very important to avoid the false solutions called local minima in the search space. We have developed a method to determine the initial values for recovering object whose images are taken by a camera that moves on circular path around the object. We also proposed a procedure to search the optimal solution from the false one. Experimental results with synthetic and real images showed that 3D recovery was stably performed with the initial values given by our method.

本文研究了一种利用非线性优化从图像序列中恢复三维形状的方法。(1)提高特征点检测与跟踪的精度本文提出了一种基于仿射几何约束和深度几何约束的特征点检测与跟踪方法。首先对包含运动视差的一对图像进行仿射变换，然后在仿射几何约束下检测图像之间的对应点。在恢复初始3D形状之后，应用深度几何约束来去除离群点并检测新的对应点。经过多次迭代，在模拟研究中从25帧图像中恢复了具有11，277个点的3D模型，并且使用真实的图像从25帧图像中恢复了具有15，751个点的3D模型。这些实验结果表明，我们的方法的有效性恢复的三维密集和精确的模型从图像序列。(2)提高非线性优化计算的稳定性从2D图像序列中恢复物体形状和摄像机运动的3D模型是一个非线性优化问题，初始值对于避免搜索空间中的局部极小值是非常重要的。我们已经开发了一种方法来确定初始值恢复对象的图像是由一个摄像机，围绕对象的圆形路径上移动。我们还提出了一个程序，以搜索最佳的解决方案，从假的。对合成图像和真实的图像的实验结果表明，该方法在给定初始值的情况下能够稳定地进行三维恢复。