PointPillars | Point Pillars is a very famous Deep Neural Network | Machine Learning library

by tyagi-iiitv Python Version: Current License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | PointPillars Summary

PointPillars is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. PointPillars has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Point Pillars is a very famous Deep Neural Network for 3D Object Detection for LiDAR point clouds. With the application of object detection on the LiDAR devices fitted in the self driving cars, Point Pillars focuse on fast inference ~50fps, which was magnitudes above as compared to other networks for 3D Object detection. In this repo, we are trying to develop point pillars in TensorFlow. Here's a good first post to familiarize yourself with Point Pillars.

Support

Quality

Security

License

Reuse

Support

PointPillars has a low active ecosystem.

It has 33 star(s) with 16 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 12 open issues and 17 have been closed. On average issues are closed in 23 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of PointPillars is current.

Quality

PointPillars has 0 bugs and 0 code smells.

Security

PointPillars has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

PointPillars code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

PointPillars is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

PointPillars releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed PointPillars and discovered the below as its top functions. This is intended to give you an instant insight into PointPillars implemented functionality, and help decide if they suit your requirements.

Builds a point pillar graph
Generates bounding boxes based on predicted regression targets
Create a new BBox object
Rotational NMS boxes
Check for focal loss
List of loss losses

Get all kandi verified functions for this library.

PointPillars Key Features

No Key Features are available at this moment for PointPillars.

PointPillars Examples and Code Snippets

No Code Snippets are available at this moment for PointPillars.

Community Discussions

Trending Discussions on PointPillars

How to disable or remove numba and cuda from python project?

QUESTION

How to disable or remove numba and cuda from python project?

Asked 2019-Aug-08 at 13:24

i've cloned a "PointPillars" repo for 3D detection using just point cloud as input. But when I came to run it, I noted it use cuda and numba. With any prior knowledge about these two, I'm asking if there is any way to remove or disable numba and cuda. I want to run it on local server with CPU only, so I want your advice to solve.

...

ANSWER

Answered 2019-Apr-08 at 14:29

The actual code matters here.

If the usage is only of vectorize or guvectorize using the target=cuda parameter, then "removal" of CUDA should be trivial. Just remove the target parameter.

However if there is use of the @cuda.jit decorator, or explicit copying of data between host and device, then other code refactoring would be involved. There is no simple answer here in that case, the code would have to be converted to an alternate serial or parallel realization via refactoring or porting.

Source https://stackoverflow.com/questions/55571679

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install PointPillars

Download the LiDAR, Calibration and Label_2 zip files from the Kitti dataset link and unzip the files, giving the following directory structure:. After placing the Kitti dataset in the root directory, run the following code.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: