pcps | CPU and GPU point cloud plane segmentation | GPU library
kandi X-RAY | pcps Summary
kandi X-RAY | pcps Summary
pcps is a small C++11 library which provides point cloud segmentation into planes with similar surface normals. Since pcps allows computing the segmentation into the GPU besides the CPU, it can segment an [organized point cloud] of 320x320 in less than 10 milliseconds on a Nvidia Geforce GTX 1060 6GB.
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pcps Examples and Code Snippets
Community Discussions
Trending Discussions on pcps
QUESTION
I searched about my problem but apparently, there is no topic about this. Any help will be appreciated.
Suppose I have a plot generated by:
...ANSWER
Answered 2020-Oct-29 at 11:45QUESTION
In my case, I have a singleton service that depends on a scoped service, which is a DbContext implementation.
The singleton service basically is a data access layer that performs the CRUD operation into the SQL server database.
In the data access layer, I have injected the IServiceScopeFactory to get an instance of my DbContext per request.
The following code block is showing a sample of the data access implementation:
...ANSWER
Answered 2020-May-21 at 09:14The changes are not saved since your are calling the async
function without await
.
Change:
QUESTION
Say I have two dictionary objects like:
...ANSWER
Answered 2020-Feb-09 at 19:09It could help a lot if you post your function, but with the infos you give I think a solution could be:
Let's say you use the function: difference_function
And the two dictionnaries you detailed are parameters for this function.
Then you could just iterae over the dictionnary : members.items()
and store in another dictionnary the discrepancy for each key of members
. Then just iterate on a list which is : [1, 2, .... size(members)]
and use each column of this list as a key for the dictionnary of discrepancies.
In python code that could be (not sure of the size/len of a dict):
QUESTION
I have two dictionaries:
...ANSWER
Answered 2020-Feb-04 at 20:58To start it might make it easier if you create a set to see if it exists first, and then if it does, find the members it belongs to.
QUESTION
I have 2 dictionary objects:
...ANSWER
Answered 2020-Jan-27 at 00:55One way to solve the task is:
QUESTION
Below is the test code:
...ANSWER
Answered 2020-Jan-27 at 00:29If possible, would take out color
from your df
so you don't duplicate with your col
vector.
You would need to order your color_fill
based on PCP
(the order based on factor, alphabetical).
Finally, would use as.character
to create a character vector instead of factor, and use as look up reference for col
.
QUESTION
We have scores for providers based on whether or not their patients are or are not in the numerator. In order to complete validation on if the patient is set (compliant or not compliant), I have been tasked with creating a report that randomly selects 50 patients from each metric. Now, I can run my query 12 times to gather 50 patients for each metric, but that is very time consuming. I thought about changing the code from Top 50 to Top 600, but returns 600 random rows, but not all the metrics are represented.
The code I am including is how it is written for just one metric at a time.
...ANSWER
Answered 2017-Jun-16 at 18:34Here is one way you could do this. This is using ROW_NUMBER and ordering the results randomly so each time you run this the results will be different.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install pcps
Ubuntu 16.04 with gcc 5.4.0.
Ubuntu 18.04 with gcc 7.3.0.
macOS High Sierra with clang.
A CMakeLists.txt is provided with this library, so in order to use it you only need to include this file in your CMake project.
For the CPU implementation there’s no external dependencies required, so the only software requirements are a C++11-compatible compiler and CMake >= 3.4:.
The OpenCL implementation can segment an [unorganized point cloud](http://pointclouds.org/documentation/tutorials/basic_structures.php) of 320x320 on a Nvidia Geforce GTX 1060 6GB more than 23 times faster than the CPU implementation:.
The CUDA implementation can segment an [unorganized point cloud](http://pointclouds.org/documentation/tutorials/basic_structures.php) of 320x320 on a Nvidia Geforce GTX 1060 6GB more than 30 times faster than the CPU implementation:.
The unit tests require [PCL](http://www.pointclouds.org) to read [PCD files](http://www.pointclouds.org/documentation/tutorials/pcd_file_format.php).
The provided 3D test viewer allows to see the output of each segmentation algorithm and to change their thresholds. The viewer requires [PCL](http://www.pointclouds.org) to read [PCD files](http://www.pointclouds.org/documentation/tutorials/pcd_file_format.php).
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