Welcome to our comprehensive guide on setting up the LiDAR Snowfall Simulation. In this article, we will walk you through the process of establishing your development environment, running the simulation, and troubleshooting any potential issues you might encounter. This simulation is primarily aimed at enhancing object detection capabilities in adverse weather conditions like snowfall.

Getting Started

Before we dive into the setup process, let’s clarify what this project entails. The LiDAR Snowfall Simulation is akin to building a snowman in your backyard but with advanced technology. Just as you would gather snow, shape it, and craft a figure, this simulation harnesses point cloud data from LiDAR to simulate snowfall patterns that improve 3D object detection.

Setup Instructions

Follow these steps carefully to set up the LiDAR Snowfall Simulation.

Step 1: Install Anaconda

• Download and install Anaconda.

Step 2: Create a Conda Environment

Open your terminal and execute the following commands:

# Create a new conda environment.
conda create --name snowy_lidar python=3.9 -y
# Activate the newly created conda environment.
conda activate snowy_lidar
# Install dependencies.
conda install matplotlib pandas plyfile pyaml pyopengl pyqt pyqtgraph scipy scikit-learn tqdm -c conda-forge -y
pip install PyMieScatt pyquaternion

Step 3: Clone the Repository

Clone the repository which includes essential libraries:

git clone git@github.com:SysCV/LiDAR_snow_sim.git --recursive
cd LiDAR_snow_sim

Step 4: Download Precomputed Snowflake Patterns

If you wish to utilize our precomputed snowflake patterns (2.3GB), execute the following commands:

wget https://www.trace.ethz.ch/publications/2022/lidar_snow_simulation/snowflakes.zip
unzip snowflakes.zip
rm snowflakes.zip

Step 5: Visualization

Run the following command to visualize LiDAR point clouds and apply various augmentations:

python pointcloud_viewer.py

Step 6: Inference on STF Dataset

To run inference on the STF dataset, follow these additional steps:

# Download our checkpoints (265MB)
wget https://www.trace.ethz.ch/publications/2022/lidar_snow_simulation/experiments.zip
unzip experiments.zip
rm experiments.zip
# Install PyTorch
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c conda-forge -c pytorch -y
# Install spconv
pip install spconv-cu113
# Build pcdet
cd libOpenPCDet
python setup.py develop
cd ..

Troubleshooting

If you encounter issues during the installation or running of the simulation, here are some troubleshooting tips:

• Ensure that your Python version matches the one specified (Python 3.9).
• Check if Anaconda and all required packages were installed correctly.
• If you face dependency errors, try updating your Anaconda environments or installing missing packages using conda or pip.
• Refer to the documentation on the respective websites of installed dependencies for specific issues.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.