Repository of the paper Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models at ML4AD @ NeurIPS 2021.

Related tags

Deep Learning machine-learning generative-model autonomous-driving
Overview

Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models

Code and supplementary materials

Repository of the paper Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models at ML4AD@NeurIPS 2021.

Online Pipeline

The left side of the videos shows the ground truth data from CARLA. On the right you see the VAE based reconstructions. Videos are accelerated.

online_pipeline.mp4
lidar_compress.mp4

Repository Structure

See the specific folders for additional information.

.
├── catkin_ws       # ROS workspace for running the online pipeline
├── evaluation      # Evaluation results
├── gan             # The GAN we use
├── lidar           # Contains the lidar preprocessing package and supplementary code
├── paper-graphics  # Code that generates some of our graphics
└── vae             # The VAE we use
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Comments
  • Online pipeline cleanup

    Online pipeline cleanup

    • Added note for decompressed Lidar visualization in RVIZ (cube)
    • Removal of clamping in decoder_lidar
    • Cleanup of script files in online pipeline
    • small adjustement width param in lidar launch file
    opened by moritz-wittig 0
  • Cleanup

    Cleanup

    • Update and consolidate README and .md files
    • Clean up several notebooks
    • Add several comments in code
    • Reorder some imports in Python scripts
    • Extend installation and setup instructions
    • NO CHANGES TO IMPORTANT CODE, only to some of my notebooks
    documentation 
    opened by jo-jstrm 0
  • Reproduction of the LiDAR results

    Reproduction of the LiDAR results

    Hello,

    I am currently looking at what you have done in this work with regard to the point cloud compression, since i am currently working on reconstruction and generation of lidar scenes myself.

    Your demo video looks really good, my attempts with depthmap based VAE always looked much weaker so far. However, I'm having trouble reproducing the result from you guys right now.

    Do you happen to remember what parameters you took for the training? Right now I use the given example command for training and resize the depthmaps to 64x128 as you wrote.

    Thanks in advance! Matthias

    question 
    opened by MatthiasReuse 6
Owner
Daniel Bogdoll
PhD student at FZI and KIT
Daniel Bogdoll
GitHub https://ml4ad.github.io/
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