4d-occ-forecasting | CVPR 2023 : Official code

by tarashakhurana Python Version: Current License: No License

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

kandi X-RAY | 4d-occ-forecasting Summary

4d-occ-forecasting is a Python library. 4d-occ-forecasting has no bugs, it has no vulnerabilities and it has low support. However 4d-occ-forecasting build file is not available. You can download it from GitHub.

CVPR 2023: Official code for `Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting'

Support

Quality

Security

License

Reuse

Support

4d-occ-forecasting has a low active ecosystem.

It has 74 star(s) with 4 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 2 have been closed. On average issues are closed in 20 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of 4d-occ-forecasting is current.

Quality

4d-occ-forecasting has no bugs reported.

Security

4d-occ-forecasting has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

4d-occ-forecasting does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

4d-occ-forecasting releases are not available. You will need to build from source code and install.

4d-occ-forecasting has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

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4d-occ-forecasting Key Features

No Key Features are available at this moment for 4d-occ-forecasting.

4d-occ-forecasting Examples and Code Snippets

No Code Snippets are available at this moment for 4d-occ-forecasting.

Community Discussions

No Community Discussions are available at this moment for 4d-occ-forecasting.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install 4d-occ-forecasting

Download nuScenes, KITTI-Odometry and ArgoVerse2.0 (code supports the LiDAR dataset, but the change to Sensor dataset is minor). (Tip: See the python scripts to see how to send the file paths.)
Create a conda environment with the given environment.yml. Additionally, install the chamferdist package given inside utils/chamferdist by navigating to that directory and doing pip install ..
All trained model checkpoints for all three datasets for both 1s and 3s forecasting are available in the models/ folder.
The given code has been tested with python3.8, CUDA-11.1.1, CuDNN-v8.0.4.30, GCC-5.5 and NVIDIA GeForce RTX 3090.

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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