Welcome to your guide on implementing the Self-supervised Sparse-to-Dense Depth Completion algorithm using PyTorch. This technique was developed by a brilliant team at MIT and harnesses the power of both LiDAR and monocular cameras for accurate depth prediction. Let’s explore how to get started!

Contents Overview

• Dependency
• Data
• Trained Models
• Commands

Step 1: Set Up Your Environment

Before you dive into the depths of depth completion (pun intended!), ensure you have the right environment. This implementation works with Python 3 and PyTorch 1.0, specifically on Ubuntu 16.04. Follow these commands to set up your dependencies:

pip install numpy matplotlib Pillow
pip install torch torchvision  # for PyTorch
pip install opencv-contrib-python==3.4.2.16  # for self-supervised training

Step 2: Download the Necessary Data

Your next stop is the data. You’ll need the KITTI Depth dataset. Download it using the following link: KITTI Depth Dataset. After downloading, you can use specific scripts to extract corresponding RGB images:

bash download_rgb_train_downloader.sh
bash download_rgb_val_downloader.sh

The RGB files will appear in the ..data/data_rgb folder. Make sure your folder structure follows this hierarchy:

self-supervised-depth-completion
└── data
    ├── data_depth_annotated
    │   ├── train
    │   └── val
    ├── data_depth_velodyne
    │   ├── train
    │   └── val
    ├── data_rgb
    │   ├── train
    │   └── val
    └── results

Step 3: Trained Models

Get your hands on pre-trained models to make things easier! Download them from here. You have the option of two types:

• Supervised training (models trained with semi-dense LiDAR ground truth)
• Self-supervised training (using both photometric loss and depth loss)

Step 4: Training Commands

You’re almost ready to start training your model! To view a full list of training options, simply run:

python main.py -h

Here are some commands you can execute:

# Train with KITTI semi-dense annotations
python main.py --train-mode dense -b 1 --input rgbd

# Train with the self-supervised framework (no ground truth)
python main.py --train-mode sparse+photo 

# Resume previous training
python main.py --resume [checkpoint-path] 

# Test the trained model
python main.py --evaluate [checkpoint-path] --val select

Troubleshooting Tips

While embarking on your journey with self-supervised depth completion, you might face a few hiccups. Here are some troubleshooting ideas:

• If you encounter dependency issues, check if Python and PyTorch versions are correctly installed.
• For any data download problems, ensure that your internet connection is stable and try downloading again.
• In case of unexpected errors during training or evaluation phases, consult the log files for error messages, as they can provide hints on what went wrong.

If you have code-related questions, please create a new issue in the repository for assistance. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Wrapping Up

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.

With these instructions, you’re now equipped to implement the self-supervised depth completion model. Dive in and discover the world of depth perception in machine learning!