Welcome to your comprehensive guide on setting up and utilizing UniDepth, a cutting-edge implementation of depth estimation. This paper is set to be a highlight at CVPR 2024, as it brings forth revolutionary solutions for depth estimation using monocular images. Let’s dive in and explore how you can install and run this method smoothly!

Installation Guide

To get started with UniDepth, you should have some requirements in place:

• Linux operating system
• Python 3.10 or higher
• CUDA 11.8 installed

Here’s how to set up your environment:

export VENV_DIR=YOUR-VENVS-DIR
export NAME=Unidepth
python -m venv $VENV_DIR/$NAME
source $VENV_DIR/$NAME/bin/activate

Next, install UniDepth and its dependencies:

pip install -e . --extra-index-url https://download.pytorch.org/whl/cu118

While Pillow-SIMD is optional, it can enhance performance:

pip uninstall pillow
CC=cc -mavx2 pip install -U --force-reinstall pillow-simd

Validating Your Installation

Once you have everything installed, you can run a test with the following command:

python .scripts/demo.py

If all is well, the output should indicate:

ARel: 5.13%

Using UniDepth for Depth Estimation

To generate depth estimations from RGB images, follow this process. It’s analogous to using a magic eye to see depth perception in flat images – a shift in perspective reveals dimensions:

1. Load the pre-trained model:

from unidepth.models import UniDepthV1
model = UniDepthV1.from_pretrained('lpiccinelli/unidepth-v1-vitl14')

2. Read in your RGB image, which is the well-prepared flat canvas:

import numpy as np
from PIL import Image
rgb = torch.from_numpy(np.array(Image.open(image_path))).permute(2, 0, 1)  # C, H, W

3. Make predictions to unveil the hidden depth structure:

predictions = model.infer(rgb)
depth = predictions['depth']

Troubleshooting Common Issues

Running into issues while setting things up? Here are some troubleshooting tips:

• If you encounter a Segmentation Fault, uninstall PyTorch using pip uninstall torch and reinstall the appropriate version using conda according to the requirements specified.
• Ensure that your CUDA versions match—both the compilation and runtime versions—by checking the PyTorch website.
• If you’re facing errors related to Triton while using xFormers, confirm that your system-wide CUDA matches the one shipped with Torch.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

A Quick Recap

Now that you’re set up and ready to go, you can explore the various models available within UniDepth. The process from model initialization to inference is as straightforward as painting layers of depth onto a canvas. Each step unveils more complexity and detail about the 3D structure within your environments.

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.

Conclusion

Congratulations on embarking on your journey with UniDepth! With your new tools at hand, you’re poised to delve deep into the world of monocular depth estimation, revealing the hidden dimensions of your data like an artist discovering depth in their masterpiece.