Voxelnet | 3D objects detections in LIDAR point clouds | Image Editing library

by steph1793 Python Version: Current License: GPL-3.0

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

kandi X-RAY | Voxelnet Summary

Voxelnet is a Python library typically used in Media, Image Editing, Tensorflow applications. Voxelnet 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.

Implementation of VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection in tensorflow 2.0.0. This project is based on the work of Qiangui Huang, (project) and Xiaojian Ma. Thanks to them for their tremendous job that allowed me to rebuild this architecture and understand the non explicit parts of the paper.

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Voxelnet has a low active ecosystem.

It has 20 star(s) with 22 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 0 have been closed. On average issues are closed in 250 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Voxelnet is current.

Quality

Voxelnet has 0 bugs and 0 code smells.

Security

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

Voxelnet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Voxelnet 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

Voxelnet 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.

It has 1884 lines of code, 89 functions and 15 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Calculate training epoch
Color a value
Convert boxes center to a rectangular box
R Convert boxes_center to a 3d box
Create a batch dataset
Augment image data
Fill the examples queue
Transform boxes to center coordinates
Helper function to align the velocity of the image
Load calibration curves
Prepare velo points
Load velocity points from file
Call the deconvolution function
Block convolution op
Convolution operator
Calculate the iou angle of boxes in boxes3
Calculate the cal_z intersection of two meshes
Performs a training step
Calls VFFM
Validate a single step
Convert a corner box to a 3d box
Predict a single step

Get all kandi verified functions for this library.

Voxelnet Key Features

No Key Features are available at this moment for Voxelnet.

Voxelnet Examples and Code Snippets

No Code Snippets are available at this moment for Voxelnet.

Community Discussions

Trending Discussions on Voxelnet

Align feature map with ego motion (problem of zooming ratio )

QUESTION

Align feature map with ego motion (problem of zooming ratio )

Asked 2021-Apr-12 at 12:17

I want to align the feature map using ego motion, as mentioned in the paper An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds

I use VoxelNet as backbone, which will shrink the image for 8 times. The size of my voxel is 0.1m x 0.1m x 0.2m(height)

So given an input bird-eye-view image size of 1408 x 1024,

the extracted feature map size would be 176 x 128, shrunk for 8 times.

The ego translation of the car between the "images"(point clouds actually) is 1 meter in both x and y direction. Am I right to adjust the feature map for 1.25 pixels?

...

ANSWER

Answered 2021-Apr-12 at 12:17

It's caused by the function torch.nn.functional.affine_grid I used.

I didn't fully understand this function before I use it.

These vivid images would be very helpful on showing what this function actually do(with comparison to the affine transformations in Numpy.

Source https://stackoverflow.com/questions/66983586

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

Vulnerabilities

No vulnerabilities reported

Install Voxelnet

Clone this repository
Compile the Cython module
Compile the evaluation code (Optional) This will compile the Kitti_eval project which I decided not to run during the training (during the dump tests). But at the end, of the training, you may run this evaluation code with the model of your choice. In the training script, it is commented. You may un-comment to run the kitti_eval during the training.
grant the execution permission to evaluation script

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 .

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