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Volume intersection with imprecise camera parameters

机译:具有不精确相机参数的体积交点

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摘要

Volume intersection is one of the simplest techniques for reconstructing 3D shapes from 2D silhouettes. 3D shapes can be reconstructed from multiple view images by back-projecting them from the corresponding viewpoints and intersecting the resulting solid cones. The camera position and orientation (extrinsic camera parameters) of each viewpoint with respect to the object are needed to accomplish reconstruction. However, even a little variation in the camera parameters makes the reconstructed 3D shape smaller than that with the exact parameters. The problem of optimizing camera parameters dealt with in this paper is determining good approximations from multiple silhouette images and imprecise camera parameters. This paper examines attempts to optimize camera parameters by reconstructing a 3D shape via the method of volume intersection. Reprojecting the reconstructed 3D shape to image planes, the camera parameters are determined by finding the projected silhouette images that result in minimal loss of area when compared to the original silhouette images. For relatively large displacement of camera parameters we propose a method repeating the optimization using dilated silhouettes which gradually shrink to original ones. Results of experiment show the effect of it.
机译:体相交是从2D轮廓重建3D形状的最简单技术之一。通过从相应的视点反向投影3D形状并将其与实心圆锥相交,可以从多个视图图像中重建3D形状。需要每个视点相对于对象的相机位置和方向(外部相机参数)来完成重建。但是,即使相机参数略有变化,也会使重建的3D形状小于具有精确参数的3D形状。本文处理的相机参数优化问题是根据多个轮廓图像和不精确的相机参数确定良好的近似值。本文研究了通过体积相交的方法通过重建3D形状来优化相机参数的尝试。将重建的3D形状重新投影到图像平面,通过找到投影的轮廓图像来确定相机参数,与原始轮廓图像相比,投影的轮廓图像会导致面积损失最小。对于较大的相机参数位移,我们提出了一种使用膨胀轮廓重复优化的方法,该轮廓逐渐缩小为原始轮廓。实验结果表明了其效果。

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