That's a good idea, it would make it faster for sure.

I agree, these tools should have an official computer vision module since so many people are using synthetic data these days.

Uou this is awesome! And it's very nicely presented in the website. I'm wondering how you mapped from the UV to the 3D model. I would like to add that feature to the addon.

It's been awhile since I've looked at the code, but take a look at the code around this https://github.com/amoffat/metabrite-receipt-tests/blob/mast... for mapping from UV space to image space

TLDR: using a KD-tree, I find the face containing the UV coordinate. Then I transform the UV coordinate to barycentric coordinates within that containing face, then put that barycentric coordinate through the local -> world -> view -> perspective transform matrices

A common approach in rendering engines to convert screen space coordinates to objects is to render a second image with light and shadow disabled where the color uniquely maps to an id. You then can uniquely identify 24 bits worth of objects without needing to maintain a KD tree.

So many likes, was not expecting that! I will be presenting this work tomorrow at MICCAI and then I will post my presentation link in the README of the repository!

Thank you for your work! Looking forward to the presentation.

I had to look up MICCAI. To others: 23rd INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING & COMPUTER ASSISTED INTERVENTION (4-8 OCTOBER 2020) https://www.miccai2020.org/en

done! added to the GitHub README

[presentation video](https://imperialcollegelondon.box.com/s/cg54pddsf2pkx4ngf4pg...)

Love it! Should maybe add a link in addition to the video "image". Was not intuitive if it is a an image from a video or a link to a video (I am not used to video "preview" on Github).

Thank you! Yeah, I found that "hack" of the "image-video" in StackOverflow. Will add the link too as suggested

Guys I won the best paper award!