从许多二维图像生成点云 [英] Generating point cloud from many 2d images

问题描述

根据我对点云工作原理的有限理解，我认为应该能够从对象外部的一组二维图像生成点云.我遇到的问题是我似乎找不到任何关于如何生成这样一个点云的例子.

From my, somewhat limited, understanding of how point clouds work I feel that one should be able to generate a point cloud from a set of 2d images from around the outside of an object. The problem that I am experiencing is that I can not seem to find any examples of how to generate such a point cloud.

推荐答案

一般来说，从 2D 图像序列重建 3D 形状是一个难题.它可以从困难到极其困难，具体取决于已知的有关相机的信息量以及它与对象和场景的关系.那里有很多信息:尝试使用谷歌搜索3D 重建图像序列"或3D 图像重建转表".这里是一篇论文，对流程及其挑战进行了很好的总结.这篇论文很好(它介绍了RANSAC"——另一个很好的搜索关键词).这个链接从面部重建的角度描述问题，但理论可以应用于这个问题.

In general, 3D shaped reconstruction from a sequence of 2D images is a hard problem. It can range from difficult to extremely difficult, depending on the amount of information that is known about the camera and it's relationship to the object and scene. There is a lot of information out there: try googling for "3D reconstruction image sequence" or "3D image reconstruction turn table". Here is one paper that gives a pretty good summary of the process and its challenges. This paper is good (and it introduces "RANSAC" - another good search keyword). This link frames the problem in terms of facial reconstruction, but the theory can be applied to this question.

请注意，对 3D 点的解释取决于对相机的外在和内在参数.外部参数指定相机相对于世界的位置和方向.内在参数将像素坐标映射到世界坐标系中的坐标.

Note that the interpretation of the 3D points is dependent upon knowledge of the camera's extrinsic and intrinsic parameters. Extrinsic parameters specify the location and orientation of the camera with respect to the world. Intrinsic parameters map pixel coordinates to coordinates in the world frame.

当外部参数和内部参数都不知道时，3D 重建精确到未知的比例因子(即可以建立相对大小/距离，但不知道绝对大小/距离).当两组相机参数已知时，3D 点的比例、方向和位置就已知了.OpenCV 文档很好地涵盖了相机校准的概念.此链接、此链接和这个链接也不错.

When neither the extrinsic nor intrinsic parameters are known, the 3D reconstruction is accurate to an unknown scale factor (i.e. relative size/distance can be established, but absolute size/distance is not known). When both sets of camera parameters are known, the scale, orientation, and location of the 3D points are known. The OpenCV documentation covers the concept of camera calibration well. This link, this link, and this link are good, too.

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