3D patch-based multi-view stereo for high-resolution imagery
3D patch-based multi-view stereo for high-resolution imagery
复制标题
基于 3D 补丁的多视图立体高分辨率图像
DOI：
10.1117/12.2309806
复制
发表时间：
2018
期刊：
影响因子：
4.2
通讯作者：
K. Palaniappan
中科院分区：
文献类型：
--
作者：
Shizeng Yao;H. Aliakbarpour;G. Seetharaman;K. Palaniappan
本文提出了一种基于图像的三维（3D）建模（也称为“多视图立体”）的改进解决方案，该解决方案输出高分辨率广域格式视频（也称为广域运动图像（WAMI））中可见的表面，该视频由密集的小3D点集组成。改进后的方法，称为3D patch-based multi-view stereo，是对PMVS1的扩展，并作为匹配、扩展和过滤过程来实现。该方法采用一系列图像帧和相应的相机参数，以及一组匹配的稀疏特征点。作为第一步，它为每个匹配的特征点制定一个小的3D补丁。然后，它根据内部每个3D点的光度一致性在3D斑块内找到最佳拟合曲面。然后在这些初始表面上递归地应用扩展和过滤过程，直到达到一定百分比的图像覆盖率。该解决方案能够精确地保留小细节，并自动检测和丢弃异常值。此外，这种方法不需要任何初始化形式的视觉船体，边界框，或有效的深度范围。我们已经在各种数据集上测试了我们的算法，包括具有精细表面细节的单个对象，以及室外遮挡的超大WAMI数据集，其中移动或静态障碍物出现在感兴趣的静态结构前面，并且存在大面积的重复纹理。
This paper proposes an improved solution to image-based three-dimensional (3D) modeling (also known as ”multi-view stereo”) that outputs surfaces visible in high-resolution wide-area format video also known as widearea motion imagery (WAMI) consisting of a dense set of small 3D points. The improved approach, named 3D patch-based multi-view stereo, is an expansion of PMVS1 and is implemented also as a match, expand, and filter procedure. This approach takes a sequence of image frames and corresponding camera parameters together with a sparse set of matched feature points. As an initial step, it formulates a small 3D patch for each of the matched feature points. It then finds the best fitted curved surface inside the 3D patch based on the photometric consistency of each 3D point inside. Expansion and filtering procedures are then recursively applied on those initial surfaces until a certain percentage of image coverage is achieved. The proposed solution is able to precisely preserve small details and automatically detect and discard outliers. Moreover this approach does not require any initialization in the form of a visual hull, a bounding box, or valid depth ranges. We have tested our algorithm on various data sets including single object with fine surface details, and outdoor occluded extremely large WAMI dataset, where moving or static obstacles appear in front of static structures of interest and large areas of repetitive texture are present.