强大的算法，从三维点云表面重建？ [英] robust algorithm for surface reconstruction from 3D point cloud?
问题描述
我试图找出什么算法有做三维范围的数据表面重建。乍一看，似乎球旋转算法的（<强>双酚A ）和泊松曲面重建的是更成熟的方法？
I am trying to figure out what algorithms there are to do surface reconstruction from 3D range data. At a first glance, it seems that the Ball pivoting algorithm (BPA) and Poisson surface reconstruction are the more established methods?
- 什么是既定的，更强大的算法在该领域比BPA和泊松曲面重建算法等？
- 推荐研究刊物？
- 有没有可用的源$ C $ C？
推荐答案
我一直面临着这个困境了几个月了，并取得了详尽的研究。
I have been facing this dilemma for some months now, and made exhaustive research.
主要有两类算法：计算几何和隐式曲面
Mainly there are 2 categories of algorithms: computation geometry, and implicit surfaces.
他们适应网格上的现有点。
They fit the mesh on the existing points.
也许这组最著名的算法是 powercrust 的，因为它在理论上是很好-established - 它保证防水网
Probably the most famous algorithm of this group is powercrust, because it is theoretically well-established - it guarantees watertight mesh.
球摆动也由IBM的专利。此外，它是不适合于点云具有不同点的密度。
Ball Pivoting is patented by IBM. Also, it is not suitable for pointclouds with varying point density.
选择一个适合的点云隐函数，然后使用步进立方体状算法来提取零集功能成网状。
One fits implicit functions on the pointcloud, then uses a marching-cube-like algorithm to extract the zero-set of the function into a mesh.
这一类的方法所使用的不同的隐函数的主要区别。
Methods in this category differ mainly by the different implicit functions used.
泊松，的霍普的和的 MPU 是最有名的算法这一类。如果你是新来的话题，我建议阅读霍普的论文，这是很说明。
Poisson, Hoppe's, and MPU are the most famous algorithms in this category. If you are new to the topic, i recommend to read Hoppe's thesis, it is very explanatory.
这一类的算法通常可以被执行，以使他们能够非常有效地处理大量的输入，以及一个可扩展其质量和LT; - >速度的权衡。他们没有受到外部干扰，不同的点密度，孔洞。其中一个缺点是，它们需要始终面向表面法线在输入点
The algorithms of this category usually can be implemented so that they are able to process huge inputs very efficiently, and one can scale their quality<->speed trade-off. They are not disturbed by noise, varying point-density, holes. A disadvantage of them is that they require consistently oriented surface normals at the input points.
您会发现少量的免费实现。然而，它取决于你是否打算将它集成到免费软件（在这种情况下，GPL许可证是可以接受的你），或进入一个商业软件（在这种情况下，你需要一个更自由的许可证）。后者是非常罕见的。
You will find small number of free implementations. However it depends on whether You are going to integrate it into free software (in this case GPL license is acceptable for You) or into a commercial software (in this case You need a more liberal license). The latter is very rare.
一个是在 VTK 。我怀疑它是难以集成（没有文档是免费的），它有一个奇怪的，过于复杂的架构，而不是专为高性能应用。也有允许输入的点云一定的局限性。
One is in VTK. I suspect it to be difficult to integrate (no documentation is available for free), it has a strange, over-complicated architecture, and is not designed for high-performance applications. Also has some limitations for the allowed input pointclouds.
看看这个的泊松实施，并分享你的经验后，它与我请。
Take a look at this Poisson implementation, and after that share your experience about it with me please.
Also: here are a few high-performance algorithms, with surface reconstruction among them.
CGAL是一个著名的3d库，但它是自由仅用于免费的项目。 Meshlab 是一个著名的应用程序GPL。
CGAL is a famous 3d library, but it is free only for free projects. Meshlab is a famous application with GPL.
另外（由2013年8月）： 图书馆 PCL 有模块致力于表面重建，并在积极的开发（并且是谷歌的夏季code的一部分）。面模块包含许多不同的算法进行重建。 PCL也有估计表面法线的能力，柜面你没有让他们提供与你的观点的数据，这一功能可以在功能中找到模块。 PCL被释放在BSD许可协议的条款，是开源软件，它是免费的商业及研究用途。
Also (Added August 2013): The library PCL has a module dedicated to surface reconstruction and is in active development (and is part of Google's Summer of Code). The surface module contains a number of different algorithms for reconstruction. PCL also has the ability to estimate surface normals, incase you do not have them provided with your point data, this functionality can be found in the features module. PCL is released under the terms of the BSD license and is open source software, it is free for commercial and research use.
这篇关于强大的算法，从三维点云表面重建？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
