Git Product home page Git Product logo

dg-mesh's People

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Forkers

paperwave

dg-mesh's Issues

Any schedule to release the code?

Hey Isabella,

Thanks for sharing the great work! Do you have any schedules to release the code? It would be great if you set an approximate time so we can stay tuned and follow up in time!

Thanks a lot !

Question on how trainable color can be defined per vertex, while there are varying number of vertices for every iteration during mesh optimization.

Hi, Thanks for the great work!

I was reading your work, and wondered how nvdiffrast can be actually implemented to render an image from a mesh that is being trained.

From my understanding, mesh from DPSR + Diff.MC during optimization can have varying number of vertices for every iteration.

In this case, how can a trainable color be defined for each vertex for every iteration?

Thank you in advance.

Question about table.1 in the paper

I am intrigued by the research presented in your paper. While going through the document, I observed that PSNR corresponding to mesh is provided in Table.1, but there seems to be a missing entry for Gaussian. From my understanding, mesh and Gaussian have a one-to-one correspondence and can be independently rendered to generate images. I was wondering if the authors could kindly explain why Gaussian's PSNR data is not included in the table. This information would be valuable for evaluating the method's performance and making comparisons. Any clarification on this matter would be greatly appreciated.

Question about grid resolution of DPSR?

Hi, I'm very interested in your work. I found that DPSR uses a regular grid for obtaining SDF. It seems a high grid resolution leads to better mesh details. I wonder about the resolution parameter of work. Thank you!

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.