MiDaS VS Graphite

Compare MiDaS vs Graphite and see what are their differences.

MiDaS

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" (by isl-org)
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MiDaS Graphite
27 46
4,089 6,831
4.1% 22.1%
2.4 9.6
3 months ago 6 days ago
Python Rust
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

MiDaS

Posts with mentions or reviews of MiDaS. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-04-25.
  • How to Estimate Depth from a Single Image
    8 projects | dev.to | 25 Apr 2024
    The checkpoint below uses MiDaS, which returns the inverse depth map, so we have to invert it back to get a comparable depth map.
  • Distance estimation from monocular vision using deep learning
    3 projects | /r/computervision | 13 Jun 2023
    Hi, I have made use of the KITTI dataset for this, and yes it depends on objects of know sizes. Here I have defined the following classes: Car, Van, Truck, Pedestrian, Person_sitting, Cyclist, Tram, Misc, or DontCare and the predictions are pretty accurate for those classes. Even if it's not the same class, it still recognizes the object since I have made use of the coco names dataset here and that is used along with YOLO for object detection. And there are several already implemented projects that make use of deep learning models trained on 2D datasets to predict 3D distance. This was one of my inspirations for this project: https://blogs.nvidia.com/blog/2019/06/19/drive-labs-distance-to-object-detection/ Furthermore, there are well-documented and researched papers like DistYOLO or MiDaS that makes use of deep learning for depth estimation
  • OMPR V0.6.10 update
    2 projects | /r/u_OMPR_App | 14 Mar 2023
    -Added AI image depth generator Create your own depth map image at a click of a button. Using the awesome MIDAS3.1 https://github.com/isl-org/MiDaS as the backend and the model "dpt_beit_large_512" for the highest quality depth map. Video and GIF depth map generators coming out next together with the Depth movie player feature.
  • AI that converts a regular 2d image to stereoscopic
    1 project | /r/ArtificialInteligence | 9 Feb 2023
    It uses MiDaS. That extension may be the most accessible way to use it at home. IDK.
  • Idea: training on magiceye images
    1 project | /r/StableDiffusion | 5 Feb 2023
    Here's the project homepage https://github.com/isl-org/MiDaS
  • MiDaS v3_1 and DiscoDiffusion
    2 projects | /r/DiscoDiffusion | 27 Dec 2022
    The problem came up after MiDaS updated to version V3_1 on Dec 24th. Although the fix works fine, with the new version there are many changes, which for me produces slightly different results. I would like to able to produce results like before. I still clone the MiDaS repo, but then set it back to the last commit before the changes in december, which is 66882994a432727317267145dc3c2e47ec78c38a.
  • File not found error
    3 projects | /r/DiscoDiffusion | 27 Dec 2022
    try: from midas.dpt_depth import DPTDepthModel except: if not os.path.exists('MiDaS'): gitclone("https://github.com/isl-org/MiDaS.git") gitclone("https://github.com/bytedance/Next-ViT.git", f'{PROJECT_DIR}/externals/Next_ViT') if not os.path.exists('MiDaS/midas_utils.py'): shutil.move('MiDaS/utils.py', 'MiDaS/midas_utils.py') if not os.path.exists(f'{model_path}/dpt_large-midas-2f21e586.pt'): wget("https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", model_path) sys.path.append(f'{PROJECT_DIR}/MiDaS')
  • A quick demo to show how structurally coherent depth2img is compared to img2img using Automatic1111.
    2 projects | /r/StableDiffusion | 12 Dec 2022
    Cool. The repo for MiDaS is here. https://github.com/isl-org/MiDaS You can see that they partially trained the model on 3D movies Here's a list of the movies that were used to train it. I wonder if they'll be training a MiDaS v 4.0 as things have moved on quite a bit since it was released in Apr 2021?
  • Boosting Monocular Depth repo
    3 projects | /r/computervision | 9 Dec 2022
    We present a stand-alone implementation of our Merging Operator. This new repo allows using any pair of monocular depth estimations in our double estimation. This includes using separate networks for base and high-res estimations, using networks not supported by this repo (such as Midas-v3), or using manually edited depth maps for artistic use. This will also be useful for scientists developing CNN-based MDE as a way to quickly apply double estimation to their own network. For more details please take a look here.
  • DepthViewer is now live on Steam :)
    3 projects | /r/virtualreality | 30 Nov 2022
    I'll make the feature to export only the depthmap .png file. If you need the depthmap .png right now you can use the MiDaS python script.

Graphite

Posts with mentions or reviews of Graphite. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-04-02.

What are some alternatives?

When comparing MiDaS and Graphite you can also consider the following projects:

stable-diffusion-webui-depthmap-script - High Resolution Depth Maps for Stable Diffusion WebUI

egui - egui: an easy-to-use immediate mode GUI in Rust that runs on both web and native

DenseDepth - High Quality Monocular Depth Estimation via Transfer Learning

Method-Draw - Method Draw, the SVG Editor for Method of Action

stablediffusion - High-Resolution Image Synthesis with Latent Diffusion Models

GimelStudio - Non-destructive, node based 2D image editor with an API for custom nodes

deeplearning4j-examples - Deeplearning4j Examples (DL4J, DL4J Spark, DataVec) [Moved to: https://github.com/deeplearning4j/deeplearning4j-examples]

Gimel-Studio - Old repo of the node-based image editor. See https://github.com/GimelStudio/GimelStudio for the next generation of Gimel Studio :rocket:

DiverseDepth - The code and data of DiverseDepth

bevy - A refreshingly simple data-driven game engine built in Rust

Insta-DM - Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency (AAAI 2021)

burn - Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals. [Moved to: https://github.com/Tracel-AI/burn]