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EP152 - NeRFiller: Completing Scenes via Generative 3D Inpainting

·3 mins

Download the paper - Read the paper on Hugging Face

Charlie: Hey everyone, welcome to episode 152 of Paper brief! I’m your host, Charlie, and with me today is the brilliant Clio, who’s got the lowdown on all things tech and AI. So, Clio, we’re diving into ‘NeRFiller: Completing Scenes via Generative 3D Inpainting’ today. Can you kick us off by explaining what this paper is all about?

Clio: Absolutely, Charlie. NeRFiller is a new methodology for filling in the missing parts of a 3D capture using generative 3D inpainting. Think about 3D scans with gaps, maybe from reconstruction failures or tricky areas that just weren’t observed. NeRFiller leverages a standard 2D visual generative model in a clever way to tackle this issue.

Charlie: Ah, got it. So it’s like using a patchwork to complete a 3D scene. But how does it make sure that the patch fits in seamlessly from all angles?

Clio: Their key discovery is what they call a ‘Grid Prior.’ By arranging images in a 2x2 grid during the inpainting process, the result is more coherent in three-dimensional space compared to treating each image separately. Then they iterate over this process to integrate these inpainted regions into a single 3D scene that makes sense from different viewpoints.

Charlie: Interesting! And does this technique only work for filling in gaps, or can it handle other scenario types as well?

Clio: It’s quite versatile. While NeRFiller excels at completing those missing chunks, it can also conjure up multiple variations for those regions, adding an element of creativity. And it’s not reliant on tight object masks or text prompts—it all comes from the scene’s context, which is pretty neat.

Charlie: That’s pretty cool, indeed. So it sounds like it can work with just about any 3D scene, right?

Clio: Exactly, from photogrammetry scans to various objects—you name it. For different settings where parts are missing or where modifications are desired, NeRFiller offers a way to envision and execute scene completions with impressive consistency.

Charlie: Clio, could you give an example of where this could be really useful?

Clio: Sure, let’s say you’ve scanned an artifact but the bottom is unobserved because it was sitting on a surface. NeRFiller would help you reconstruct that hidden part in a believable way, which could be invaluable for things like digital archiving or virtual reality.

Charlie: Got it. And compared to other methods out there, how does NeRFiller hold up?

Clio: The team compared NeRFiller to other existing solutions and found it produces more consistent and plausible 3D scene completions. That’s a big win for anyone looking to create or restore 3D models with high fidelity.

Charlie: Sounds like a promising advance for 3D scene reconstruction and much more. Thanks for the insights, Clio! As always, folks, check our episode notes for links to the full paper and more. Catch you next time on Paper brief!

Clio: My pleasure, Charlie! It’s always exciting to see how new techniques can push the boundaries in 3D modeling and AI. Take care, everyone!