Welcome to this detailed guide on the ECoDepth project, an unprecedented approach to monocular depth estimation using diffusion models, set to be unveiled at **CVPR 2024**.

Project Overview

ECoDepth leverages cutting-edge techniques to improve depth estimation from single images. It addresses challenges faced in existing methodologies by strategically conditioning diffusion models. This project has garnered attention and is featured at the IEEE CVPR conference.

For more information about ECoDepth, check out the Project Page or view the arXiv paper.

Getting Started

Follow these steps to set up ECoDepth on your local machine and begin your journey into depth estimation.

Installation

• Clone the repository:

git clone https://github.com/Aradhye2002/EcoDepth

• Navigate into the project directory:

cd EcoDepth

• Create a new conda environment:

conda env create -f env.yml

• Activate the environment:

conda activate ecodepth

Dataset Setup

Preparing your dataset is crucial for the effective training of models.

• For dataset preparation, refer to the guide for NYUv2 and KITTI found here.
• After downloading, adjust the paths in appropriate bash files to your dataset’s location, or create symbolic links:

cd depth
mkdir data
cd data
ln -s path_to_kitti_dataset kitti
ln -s path_to_nyu_dataset nyu

Using Pre-trained Models

To utilize pre-trained weights:

• Download the pretrained weights from this link and save the .ckpt files inside repo/root/depth/checkpoints.
• Additionally, download the v1-5 checkpoint for stable-diffusion from here and place it in the checkpoints directory.

Inference Process

Now that your model is set up, you can perform inference using the provided scripts. Make sure to specify the image or video paths:

• For outdoor scenes, run:

bash infer_outdoor.sh

• For indoor scenes, run:

bash infer_indoor.sh

Model Evaluation

To evaluate the model’s performance:

• Run evaluations on the NYUv2 dataset:

bash test_nyu.sh path_to_saved_model_of_NYU

• And for the KITTI dataset:

bash test_kitti.sh path_to_saved_model_of_KITTI

Training the Model

If you want to train the models yourself, make sure to specify batch sizes according to your GPU resources:

bash train_nyu.sh

bash train_kitti.sh

Troubleshooting

If you encounter any issues during setup or operation, consider the following tips:

• Make sure your dataset paths are accurate and accessible.
• Check that your conda environment was successfully created and activated.
• If any scripts do not run, ensure you have executable permissions.
• Refer back to setup guides and verify step completion.

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Final Thoughts

At **[fxis.ai](https://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.

Conclusion

With ECoDepth, you’re stepping into a realm of innovative depth estimation technology. By following the setup and usage guides provided, you can make significant contributions to the field. Happy coding!