emergent-occ-forecasting | 22 paper , `` Differentiable Raycasting

by tarashakhurana Python Version: Current License: MIT

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

kandi X-RAY | emergent-occ-forecasting Summary

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

Implementation of the ECCV '22 paper, "Differentiable Raycasting for Self-supervised Occupancy Forecasting"

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emergent-occ-forecasting has a low active ecosystem.

It has 16 star(s) with 2 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

emergent-occ-forecasting has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of emergent-occ-forecasting is current.

Quality

emergent-occ-forecasting has no bugs reported.

Security

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

License

emergent-occ-forecasting is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

emergent-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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emergent-occ-forecasting Key Features

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emergent-occ-forecasting Examples and Code Snippets

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Vulnerabilities

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Install emergent-occ-forecasting

Download nuScenes dataset, including the CANBus extension, as we will use the recorded vehicle state data for trajectory sampling. (Tip: the code assumes they are stored under /data/nuScenes.)
Download ONCE dataset. (Tip: the code assumes they are stored under /data/once)
Install packages and libraries (via conda if possible), including torch, torchvision, tensorboard, cudatoolkit-11.1, pcl>=1.9, pybind11, eigen3, cmake>=3.10, scikit-image, nuscenes-devkit. For your convenience, and environment.yml file has also been provided. (Tip: verify location of python binary with which python.)
Compile code for Lidar point cloud ground segmentation under lib/grndseg using CMake.

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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