Marigold: A Cutting-Edge Approach to Monocular Depth Estimation
Overview
Marigold is a pioneering project that explores the use of diffusion-based image generators, commonly used for creating synthetic images, in the field of monocular depth estimation. It garnered attention for being an oral presentation and a candidate for the Best Paper Award at the prestigious CVPR 2024 conference.
Core Concept
At the heart of Marigold is a diffusion model designed to leverage the vast visual knowledge embedded in contemporary generative image models. This model is based on a technology called Stable Diffusion. By fine-tuning it with synthetic data, Marigold can effectively estimate depth in images using only a single camera view. This "zero-shot transfer" capability enables it to provide top-notch depth estimation even with new, unseen data.
Key Innovations
Marigold is notable for its ability to repurpose existing technology (diffusion-based image generators) for an application it wasn't initially designed for (depth estimation). This involves fine-tuning the model with synthetic data which allows it to recognize and predict depth from images it hasn't encountered before.
Recent Updates
Throughout 2023 and 2024, the Marigold team has made several significant developments:
- May 28, 2024: Released the training code to the public.
- March 23, 2024: Announced an accelerated version called LCM v1.0, which allows for faster inference.
- December 2023: Contributed to the community efforts by making Marigold a part of the Diffusers pipeline and updated the licensing to the Apache License, Version 2.0.
How to Use
Marigold can be used in several ways:
- Online Demonstration: Users can try out Marigold at Hugging Face, a platform that hosts a free online demo.
- Local Setup: For those with the necessary hardware, Marigold can be tested locally by running a docker image or on Google Colab, an online Python notebook environment.
- Development: Developers can clone the code repository to customize and develop new features or integrate Marigold into other applications.
Technical Setup
Marigold's official code has been validated on systems running Ubuntu with high-end NVIDIA graphics cards, but it also supports installation on Windows systems via WSL2. The installation can be conducted using tools like Mamba or pip, which help manage software environments.
Running Inference
To test out Marigold's depth estimation capabilities, users can prepare images by either downloading sample data or using their own. The inference script is designed to adapt between accuracy and speed, allowing users to tweak the settings based on their hardware capabilities.
Conclusion
Marigold represents a significant step forward in using advanced image generation techniques in practical applications such as depth estimation. Its ability to derive depth information from single images opens up possibilities in fields like autonomous driving and 3D modeling. The project is a testament to innovative thinking in computer vision, offering a glimpse into the future of how AI can be adapted and repurposed for varied technological challenges.