LIDAR技术给出了我们见过的一些最大数据量的空间数据世界。我与之合作的许多点云FME一直是数十个千兆字节。来自十年前的地方，我认为我永远不需要五个以上的千兆字节的存储空间。。。这是一个非常令人印象深刻的统计数据。
尺寸继续增长。2007年，我们的用户看到了点云，代表每平方米为空降数据。在2013年，我们看到了更像eight points per square meter对于相同的数据。
那么如何将这种大量的数据用作优势？以下是我们看到人们最令人兴奋的数据类型的14种方式。
1. Translate between point cloud and other data formats
很像CAD，GIS，RASTERS等数据类型，LIDAR数据不限于单一格式。点云格式我们已经看到亚搏在线include:
- ASTM E57
- las（压缩或不）
- Oracle Spatial Point Cloud
- Bentley Pointools POD
- 点云XYZ.
- RIEGL Laser Scan Database
- Terrasolid TerraScan
- Z + F LASERCONTROL ZFS
最流行的格式是las和xyz - 但像我妈妈曾经告诉过我一样，“受欢迎程度让你无处可去。”好的，也许那种建议是为了一个比较的青少年，但我的观点是现代数据处理需要灵活性。您需要能够以各种格式工作，不仅翻译“点云到点云”，还可以将“点云到任何东西”和“任何要点云”。
Maybe you have an XYZ point cloud and you want a .lasd file so you can use it in ArcGIS. Or maybe, like in this example, you need to convert your LAS point cloud to E57. The ability to translate your point cloud data between formats is more important than ever.
2. Combine point clouds with other data formats
当您将LIDAR数据与其他数据类型组合时，在Spacetime中打开了一个神奇的鸿沟。无论是CAD，GIS，栅格，矢量数据，3D几何，另一个点云，重建海盗地图，或以上所有点云，您的点云数据都刚刚变得有趣。
在此示例中，我们将E57 Point云组合使用了项目边界的DGN文件和ECW光栅图像。我们从栅格和边界应用了从传染媒介文件的颜色，并生成了一个纹理的表面，您可以轻松地发送到3D PDF中的伟大阿姨默特尔。如果您需要，您也可以添加一些3D构建几何形状。再见，无色和无法区分的圆点。
3. Inspect LiDAR point components and values
That multi-gigabyte beast on your hard drive has no hope of transforming into a handsome prince if you can’t decipher it. A key step in any transformation is检查，因此打开您的点云以查看以了解所涉及的组件。
With the right inspection tool, even the most enormous point cloud datasets will start to make sense.
4.更改点云的坐标系
LiDAR data has come to us in a variety of coordinate systems (UTM, StatePlane, etc).
如果您需要将点云投影到地图上或与其他数据组合，可以reproject它到另一个系统就像任何其他类型的空间数据一样。
5.瓷砖LIDAR数据加快处理时间
平铺是将输入功能（点）切成一系列瓷砖时。
If you have a big point cloud and a lot of transformations to do,try tiling it并使用并行处理。It’ll make your old transformation time look like a sloth on sedatives. Of course, tiling is also useful if you need to generate smaller pieces of the point cloud for delivery.
6. Clip to a specific region in a point cloud
1.2 billion points isn’t a crazy number when dealing with LiDAR data. In fact, the word “billion” is pretty common. Chances are, you might not need all of those points in your output or analysis. Maybe you only want a specific region, like the area around a street.
剪裁是抛出在定义边界之外的点。这可以是Mega-uki-yearful，创建易于使用的尺寸。如果您提供3D Solid作为Clipper Shape，您也可以执行立方剪辑。
在这个例子中，我们已经应用了一个立方剪辑来清理一切，而是从街道的点云中清理一切。
7. Reduce the number of points in a LiDAR dataset
“That’s great and all,” I hear you saying, “but what if I do need the whole region?”
所以也许你想要一个可管理的大小来工作，但你不想夹出一个重要的特征，让地球看起来像巨型轮蒸汽机组压碎。减少点云降低其整体体积;例如，通过删除每个第n个点。
在这里我们已经巨大变薄了点云without losing the gist of the dataset, resulting in sped-up processing time.
8. Create a surface model from a point cloud
If you’re interested in topography, you can make a 3D model out of your LiDAR dataset. Our stats show that表面模型转换是最受欢迎的点云任务之一。
在用RGB和DEM栅格覆盖这一点云，我们能够创建一个有意义的曲面模型并理解原始的积分球。
9.按组件值拆分LIDAR数据
分裂点云手段extracting points based on the value of a component。You cansplit by any component- 分类，强度，颜色，无论如何。
In this point cloud, we can extract the yellow road markings by splitting the point cloud by color. The output will be a mini point cloud containing just the road lines, which we can turn into a vector feature.
10.计算和更新点云范围
当我说计算扩展区时，我的意思是单个收集各种各样的元数据。检查所有点并找出LIDAR数据集中存在的组件可能会有所帮助计算每个组件的最小值和最大值所以你知道你正在处理什么样的数据。
You can强制执行真正的范围通过检查LIDAR文件的标题并将该信息与实际点进行比较来在数据上。
11. Divide LiDAR data along a line
给定一行和点云作为输入，可以生成点云切片（或配置文件）沿该行，以便您可以使用它们进行分析。
Slicing and profilingis a great way to reduce the overall size of your LiDAR dataset and focus on what you need. In this example, we retrieved slices along a highway line and left the rest of the point cloud out, since the area around the highway is all we care about.
12. Set point cloud components manually
在编写点云时，您可以手动设置点上的组件 - 颜色为最流行的示例。只需在Microsoft Paint中选择Paintbrush工具，在第一个点上绘制，然后选择另一种颜色并绘制第二点，并继续这样做，直到你达到整个十亿......开玩笑。通过覆盖光栅上的点云来智能地进行智能。
Of course, setting other components also leads to interesting results. For example, if you have a raster that has classes, you can transfer the classes over to your point cloud.
13. Apply point-by-point calculations to LiDAR data
Expanding on the last point, the values you use to set the components manually don’t need to be taken from, say, a raster. You can also leverage attributes and parameters and根据计算设置组件值。
此示例使用计算来为洪水级预测提供良好的可视化。我们设置了颜色组件，因此每一点低于一定的高程是蓝色的，并且上面的每个点都从覆盖光栅上取出颜色。
14.过滤LIDAR数据点点
You can alsouse calculations to filter your point cloud。例如，我们知道道路标志非常反思。(Unless you live in the woods or in one of those cities that doesn’t have any streets – in which case, this is me informing you that road signs are very reflective.) This reflectiveness would be represented in the intensity component of a LiDAR dataset. So if we create a filtering expression around the intensity component, we can extract the road signs.
You can see the road signs highlighted in red, having been extracted from the original point cloud behind it.
The potential for turning your point clouds into advanced and useful datasets is sure to keep growing, and there are certainly more than fourteen ways to work with this data already. What kinds of translations and transformations do you do with your LiDAR data? What do you find the most challenging?
Tiana Warner
Tiana is a product marketing manager at Safe Software. Her background in computer programming and creative hobbies led her to be one of the main producers of creative content for Safe Software. Tiana spends her free time writing fantasy novels and riding her horse, Bailey.
在使用点云时，我总是发现自己正在寻找特定工具......一个允许您转过点云的工具，并采样高点。只是一个最终的样本，以从最高点提取倒点云的表面。只是为了浏览所有上述地面特征一分钟，从地面开始。当天我每天使用激光乐队时，我从未发现该工具。现在，几年后，我想知道它是否曾进入生产......。
Hi Dustan,
我不确定我完全明白你想实现什么，但我们绝对可以非常轻松地反转云 - 检查上面的项目＃13 - 我们可以将表达式应用于点云中的点组件和坐标，所以我只需要拍摄，说，点云的最大z并从中减去每个点的z值 - 这将为我们提供反向点云。我们只需要一个变压器。
For sampling the high (or low) spots I would use clipping with a 3D solid (see item #6 above) – but here I am not sure, which approach you would use – would look for local maxes or mins, or just use a certain value for the whole dataset, or something totally different – maybe, if you could explain to me with a little bit more details your scenario, I would be able to say whether (or how) this can be achieved with FME.
以下是与两个点云的图片的链接 - 原始和反转（和可见性的偏移）：
https://dl.dropboxusercontent.com/u/51907218/pc%26invertedpc.jpg.
随意与我联系与我与FME有关的任何问题。
Best regards,
DMitri.
Dmitri花园|Scenario Creation and Testing Analyst
亚搏在线安全软件公司
T 604.501.9985 x 276
dmitri.bagh@safe.com|http://www.fmeusercentral.com.|//www.baooytra.com
I would like to be able to create a topographical survey from a LiDAR data set, but without having to remove all of the noise, and above ground information. When I would view the under-side of the point cloud, it would be a very clear view of the information. However, in the software I was using at the time (Cyclone) in order to create an accurate surface model you were required to extract the points you wanted to use in the creation of the mesh. Unless is was featureless, this could be very time consuming. I had always imagined a routine that would be able to take all of the busy work out of creating a mesh….
If I were to use this software to accomplish a task much like DEMO 4 could the software also put a point on each sign and have them listed as sign1, sign2, ect so that I could click each one and be sent to the signs location? I need to be able to put a point at each signs location that can be ultimately clicked on to bring up information on the sign (with in a different software)
在视频中，它在此网页上的演示4是示例14
Hi Brandyn,
您可以使用FME PointCloudExpressionEvaluator transformer to add metadata to every point based on a condition (ex. which sign that point belongs to). FME 2014 has greater flexibility with point cloud components, meaning you can certainly add the name of the sign and any other metadata you want shown during inspection. Clicking the point in your destination software will bring up all the information on that sign. As far as being sent to the sign’s location, that capability depends on what software you’re using to inspect the point cloud — but you can certainly identify the location of each point with FME.
如果您打算在每个符号的位置进行单点，则可以使用PointCloudFilter变压器将点分为每个符号的单独点云。然后，您可以使用CenterpointReplacer或PointCloudCoercer变压器来获得一个点来表示每个符号。
I hope that answers your question! Don’t hesitate to contact us if you would like more information.
Sincerely,
Tiana Warner
在让LIDAR陷入误解或论坛8的任何提示？
我们在MicroStation中得到了小的XYZ文件，LAS失败了。XYZ没有强度，看起来没用。
我们在Forum 8中有小LIDAR路段，并在云上尝试了他们的道路建筑功能，失败了。认为云格式错误。
嗨大卫，
The native MicroStation point cloud format is POD, but it can convert LAS files into POD easily with the necessary components. It sounds like something might be wrong with your source data, in which case we can have a look. Are you able to send us a sample?http://fmepedia.亚搏在线safe.com/knowledgesubmitcase.
谢谢，
Tiana
你好，
这个软件是否能够转换为本地投影？例如，WGS84到Rso Kertau马来西亚。
谢谢
Yes, FME supports over 5000 coordinate systems including the ones you mentioned. Information on working with coordinate systems can be found here:http://docs.亚搏在线safe.com/fme/html/fme_coords_systems/fme_coords_systems.htm.
Cheers,
Tiana
你好，
我有点云分类已经在Terascann，所有点都被分配给五类之一。但现在我想改变班级号码。从19到8。此时任何时候都被归类为8，我想将分类为19级分类为8的所有点。
我怎样才能在FME中进行？我可以吗？
问候
Pawel.
嗨Pawel，
Yes, this is a job for the PointCloudExpressionEvaluator transformer. You’d create a workspace that reads your TerraScan data, then set up that transformer to change the Class based on a formula. For example: “@if(@Component(classification)==19, 8,@Component(classification))” checks if a point’s classification is 19 and if so, changes it to 8.
Hope this helps!
Tiana
有用！
Thanks Tiana,
你是摇滚！
How about data from Faro 3D software called Scene? File extension is .FLS.
伟大的文章！
但是，您忘记提及另一种可能的点云使用。
如今有像这样的工具http://scenemark.com您可以在网站上使用数百万或甚至数十亿个积分放入整个数据集。可能是向客户展示数据的最快方式。
伟大的网站和一般信息。应该按下点云的主题是噪声删除，特别是“玉米行”这些是由1.1到1.2标准数据格式与1.4格式碰撞的矛盾引起的。噪音标记为1.1标准，现在1.4具有低噪音和高噪音。人们希望看到ESRI和其他人用ASPRS坐下来，并解决GIS / LIDAR格式和过渡格式。
我可以使用此分类LIDAR数据吗？我想知道