View Dependent

View Dependent

Welcome to the View Dependent podcast, where we talk to leaders in the 3D reconstruction space of Radiance Field technologies, focusing on methods like Neural Radiance Fields (#NeRFs), Gaussian Splatting (#3DGS), Gaussian Ray Tracing (#3DGRT), and Diffusion based models.

  1. 13 may

    Jan Held on Triangle Splatting, Mesh Splatting, and Radiance Fields In Traditional Pipelines

    Jan Held joins View Dependent to walk through his research into alternative primitives for radiance fields, with a focus on what each choice enables for traditional rendering pipelines. We cover the full arc — 3D Convex Splatting → Triangle Splatting → Triangle Splatting+ → Mesh Splatting → Nexels — and what each step solved. Then we get into his work at SpAItial on world models that reconstruct explorable scenes from a single image, and where reconstruction and generative approaches each fit.

  2. 23/12/2024

    Gaussian Splatting with Bernhard Kerbl

    Co-hosts Michael Rubloff and MrNeRF speak to co-first author of Gaussian Splatting, Bernhard Kerbl. Papers 3D Gaussian Splatting for Real Time Radiance Field Rendering Taming 3DGS: High-Quality Radiance Fields with Limited Hierarchical 3D Gaussian Representation for Real-Time Rendering

  3. 05/12/2024

    Radiance Fields and The Future with Jon Barron

    Co-hosts Michael Rubloff and Mr. NeRF speak to co-author of NeRF and Google researcher, Jon Barron. This is an exciting episode covering everything from the discovery of NeRF, subsequent Radiance Field methods, things yet to come, and more!

  4. 04/12/2024

    Gaussian Splatting Surface Reconstruction with Antoine Guédon

    Co-hosts Michael Rubloff and Mr. NeRF talk to researcher, Antoine Guédon about his work on Gaussian Splatting to Mesh. Check out Antoine's work here: https://anttwo.github.io/ Gaussian Frosting Blender Plugin: https://github.com/Anttwo/sugar_frosting_blender_addon

  5. 18/11/2024

    Gaussian Splatting on Mac and AMD with Brush

    Co-hosts Michael Rubloff and Mr.NeRF talk to Arthur Brussee, whose creation Brush, is allowing people to train Gaussian Splatting in a web browser. If you've been wanting to try 3DGS on Mac, AMD, Intel or NVIDIA cards, this episode is for you! Brush (On Chrome only): https://arthurbrussee.github.io/brush-demo/ GitHub page: https://github.com/ArthurBrussee/brush Rerun Data Visualizer: https://rerun.io/ CAT3D: https://cat3d.github.io/

  6. 18/11/2024

    MonST3R and Spann3R

    Co-hosts Michael Rubloff and MrNeRF speak to the authors of MonST3R and Spann3R, Junyi Zhang and Hengyi Wang. https://monst3r-project.github.io/ https://hengyiwang.github.io/projects/spanner

  7. 18/11/2024

    Compressing Gaussian Splatting with Wieland Morgenstern

    Co-hosts Michael Rubloff and MrNeRF speak to Wieland Morgenstern about Gaussian Splatting compression and his paper, Self Organizing Gaussians. Self Organizing Gaussians can compress 90% of a .ply and is what Nerfstudio currently uses as their compression method. Project: https://fraunhoferhhi.github.io/Self-Organizing-Gaussians/ Code: https://github.com/fraunhoferhhi/Self-Organizing-Gaussians

  8. 18/11/2024

    Gaussian Splatting in SideFX Houdini with GSOPS

    This week on The View Dependent Podcast, co-hosts Michael Rubloff and MrNeRF speak to David Rhodes and Ruben Diaz of GSOPS 2.0. We'll discuss why Radiance Field methods, such as 3DGS are perfect fits for Houdini, what you can do within Houdini, and more! GSOPs Github: https://github.com/david-rhodes/GSOPs... GSOPs Discord Link:  https://discord.com/invite/BEm46CDjAg Video shown in Episode:  https://www.youtube.com/watch?v=5V7mBuVxlt4&t=0s

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Welcome to the View Dependent podcast, where we talk to leaders in the 3D reconstruction space of Radiance Field technologies, focusing on methods like Neural Radiance Fields (#NeRFs), Gaussian Splatting (#3DGS), Gaussian Ray Tracing (#3DGRT), and Diffusion based models.

Información

  • Creador
    View Dependent
  • Años de actividad
    2023 - 2026
  • Episodios
    19
  • Clasificación
    Apto
  • Copyright
    © View Dependent
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