Depth from accidental motion using geometry prior
Depth from accidental motion using geometry prior
复制标题
使用几何先验得出意外运动的深度
DOI：
10.1109/icip.2015.7351589
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
In
中科院分区：
文献类型：
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作者：
Sunghoon Im;Gyeongmin Choe;Hae;In
我们提出了一种从小的相机运动中重建密集 3D 点的方法。我们首先通过运动结构 (SfM) 方法和单应性分解来估计稀疏 3D 点和相机姿态。尽管估计点通过捆绑调整进行了优化并提供了可靠的精度，但重建点很稀疏，因为它很大程度上取决于提取的场景特征。为了解决这个问题，我们提出了一种深度传播方法，该方法使用图像先验的颜色和初始点先验的几何形状。我们的方法的主要好处是，我们可以通过使用从初始点估计的表面法线轻松处理具有相似颜色但不同深度的区域。我们将深度传播框架设计成成本最小化过程。成本函数是线性设计的，这使得我们的优化变得容易处理。我们通过使用各种现实世界的例子与传统方法进行比较来证明我们的方法的有效性。
We present a method to reconstruct dense 3D points from small camera motion. We begin with estimating sparse 3D points and camera poses by Structure from Motion (SfM) method with homography decomposition. Although the estimated points are optimized via bundle adjustment and gives reliable accuracy, the reconstructed points are sparse because it heavily depends on the extracted features of a scene. To handle this, we propose a depth propagation method using both a color prior from the images and a geometry prior from the initial points. The major benefit of our method is that we can easily handle the regions with similar colors but different depths by using the surface normal estimated from the initial points. We design our depth propagation framework into the cost minimization process. The cost function is linearly designed, which makes our optimization tractable. We demonstrate the effectiveness of our approach by comparing with a conventional method using various real-world examples.