Welcome to the exciting world of 360SD-Net, a project developed by Ning-Hsu Wang and colleagues, which allows for innovative 360-degree stereo depth estimation. In this article, we will guide you through the setup and usage of this powerful tool, transforming complex scientific concepts into user-friendly instructions.

Project Overview

360SD-Net enhances stereo depth estimation using a learnable cost volume approach. The project has gained recognition in the field, culminating in a presentation at the ICRA 2020 conference.

Setting Up Your Environment

Before diving into the depths of stereo learning, you will need to prepare your workspace. Here’s how to create the necessary directory structure and install the dependencies:

• Open your terminal and setup a directory for all experiments:

# SETUP REPO
git clone https://github.com/albert100121/360SD-Net.git
cd 360SD-Net
mkdir output
cd conda_env
conda create --name 360SD-Net python=2.7
conda activate 360SD-Net
conda install --file requirement.txt

• Next, you will need to download the MP3D and SF3D datasets:

cd .data
# request download MP3D Dataset
unzip MP3D Dataset
# request download SF3D Dataset
unzip SF3D Dataset

Data and Directory Structure

It’s crucial that your data is correctly structured for optimal results. The following layout is recommended:

• MP3D Dataset:

data
├── MP3D
│   ├── train
│   │   ├── image_up
│   │   ├── image_down
│   │   └── disp_up
│   ├── val
│   └── test
│       ├── image_up
│       ├── image_down
│       └── disp_up

• SF3D Dataset:

data
├── SF3D
│   ├── train
│   │   ├── image_up
│   │   ├── image_down
│   │   └── disp_up
│   ├── val
│   └── test
│       ├── image_up
│       ├── image_down
│       └── disp_up

Training the Models

Once everything is set up, you can start the training procedure. It’s more like preparing a dish than a technical process; ensure all the ingredients are in place before putting it in the oven!

• For MP3D Dataset:

python main.py --datapath data/MP3D/train --datapath_val data/MP3D/val --batch 8

• For SF3D Dataset:

python main.py --datapath data/SF3D/train --datapath_val data/SF3D/val --batch 8 --SF3D

Testing the Models

After training, you’ll want to put your newly trained model to the test, similar to a final taste test after cooking.

• For MP3D Dataset:

python testing.py --datapath data/MP3D/test --checkpoint checkpoints/MP3D_checkpoint/checkpoint.tar --outfile output/MP3D

• For SF3D Dataset:

python testing.py --datapath data/SF3D/test --checkpoint checkpoints/SF3D_checkpoint/checkpoint.tar --outfile output/SF3D

• For Real World Data:

python testing.py --datapath data/realworld --checkpoint checkpoints/Realworld_checkpoint/checkpoint.tar --real --outfile output/realworld

• For small inference:

python testing.py --datapath data/inference/MP3D --checkpoint checkpoints/MP3D_checkpoint/checkpoint.tar --outfile output/small_inference

Converting Disparity to Depth

If you need to convert disparity to depth, it’s like turning raw ingredients into a final dish:

python utils/disp2de.py --path PATH_TO_DISPARITY

Troubleshooting

If you encounter issues, here are some troubleshooting tips:

• Make sure your GPU has at least 32 GB of memory for training. You might face out-of-memory errors otherwise.
• For testing, a GPU with 12 GB (like the 1080Ti) should be sufficient for a 512 x 1024 image. Anything less may cause performance issues.
• If your directory structure seems off, double-check that your data is linked according to the earlier instructions.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

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.