How to Estimate Depth from a Single Image

1. fiftyone

   1 32 9,461 10.0 Python

   Refine high-quality datasets and visual AI models

   

   We will use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for data management and visualization, and scikit-image for evaluation metrics.

2. InfluxDB

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   InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.

   

3. scikit-image

   2 10 6,241 9.6 Python

   Image processing in Python

   

   We will use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for data management and visualization, and scikit-image for evaluation metrics.

4. fast-depth

   3 2 947 0.0 Python

   ICRA 2019 "FastDepth: Fast Monocular Depth Estimation on Embedded Systems"

   

   💡For this walkthrough, we will only use the NYU depth v2 portions. NYU depth v2 is permissively licensed for commercial use (MIT), and can be downloaded from Hugging Face directly.

5. replicate-javascript

   4 13 548 7.7 TypeScript

   Node.js client for Replicate

   

   In this section, we’ll show you how to generate MDE depth map predictions with both DPT and Marigold. In both cases, you can optionally run the model locally with the respective Hugging Face library, or run remotely with Replicate.

6. DORN

   5 1 468 - Jupyter Notebook

   

   For a long time, the state-of-the-art models for monocular depth estimation such as DORN and DenseDepth were built with convolutional neural networks. Recently, however, both transformer-based models such as DPT and GLPN, and diffusion-based models like Marigold have achieved remarkable results!

7. DenseDepth

   6 5 1,592 0.0 Jupyter Notebook

   High Quality Monocular Depth Estimation via Transfer Learning

   

   For a long time, the state-of-the-art models for monocular depth estimation such as DORN and DenseDepth were built with convolutional neural networks. Recently, however, both transformer-based models such as DPT and GLPN, and diffusion-based models like Marigold have achieved remarkable results!

8. MiDaS

   7 28 4,883 2.6 Python

   Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"

   

   The checkpoint below uses MiDaS, which returns the inverse depth map, so we have to invert it back to get a comparable depth map.

9. SaaSHub

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   SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

   

10. Marigold

    8 4 2,681 5.0 Python

    [CVPR 2024 - Oral, Best Paper Award Candidate] Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation

    

    git clone https://github.com/prs-eth/Marigold.git

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