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Computer Vision - ObjFiller-3D Consistent Multi-view 3D Inpainting via Video Diffusion Models
Hey PaperLedge crew, Ernis here, ready to dive into some seriously cool tech! Today, we're talking about making 3D objects whole again – like digital pottery, but with algorithms!
Imagine you've got a 3D scan of, say, a beautiful vase. But uh oh, part of it is missing – maybe the handle got chopped off in the scan. Existing methods for filling in those gaps often use 2D images from different angles to "guess" what's missing. Think of it like patching a hole in your jeans using scraps of fabric – if the scraps don't quite match, you end up with a messy, uneven repair. That's what happens when those 2D guesses don't quite line up, resulting in blurry textures or weird seams in your 3D object. It’s like a digital Frankenstein!
That's where ObjFiller-3D comes to the rescue! These researchers said, "Hold on, there's a better way!" They realized that instead of relying on individual 2D images, they could borrow techniques from video editing. Think about how video editing software can seamlessly remove objects from a scene or fill in missing frames. They adapted those techniques to work directly on 3D objects.
Now, you might be thinking: videos and 3D objects are totally different! And you'd be right. But the team figured out how to bridge that gap, cleverly adapting the video editing algorithms to understand and work with the 3D space. Imagine trying to translate a poem from English to Japanese - it is not a direct word for word translation, but rather understanding of the poem's intent and meaning. That's essentially what they did!
And here’s a cool twist: they also introduced a "reference-based" approach. So if you're trying to fix that broken vase handle, you could show the system a picture of a similar vase with a perfect handle. ObjFiller-3D can then use that reference to make a much better, more realistic repair. It's like having a skilled artisan guiding the computer!
"Instead of employing a conventional 2D image inpainting model, our approach leverages a curated selection of state-of-the-art video editing model to fill in the masked regions of 3D objects."
The results are pretty impressive. The researchers compared ObjFiller-3D to other methods, and it consistently produced more detailed and accurate reconstructions. They used some fancy metrics like PSNR and LPIPS, but basically, ObjFiller-3D's results looked way better to the human eye. They saw a PSNR of 26.6 compared to NeRFiller's 15.9 and a LPIPS of 0.19 compared to Instant3dit's 0.25!
Why does this matter?
This tech has the potential to revolutionize how we work with 3D objects, making it easier than ever to create, repair, and share them.
So, here are some things that are swirling around in my mind:
Definitely some food for thought! Check out the project page at https://objfiller3d.github.io/ and the code at https://github.com/objfiller3d/ObjFiller-3D and let me know what you think! Until next time, keep those neurons firing!
Credit to Paper authors: Haitang Feng, Jie Liu, Jie Tang, Gangshan Wu, Beiqi Chen, Jianhuang Lai, Guangcong Wang
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By ernestasposkusHey PaperLedge crew, Ernis here, ready to dive into some seriously cool tech! Today, we're talking about making 3D objects whole again – like digital pottery, but with algorithms!
Imagine you've got a 3D scan of, say, a beautiful vase. But uh oh, part of it is missing – maybe the handle got chopped off in the scan. Existing methods for filling in those gaps often use 2D images from different angles to "guess" what's missing. Think of it like patching a hole in your jeans using scraps of fabric – if the scraps don't quite match, you end up with a messy, uneven repair. That's what happens when those 2D guesses don't quite line up, resulting in blurry textures or weird seams in your 3D object. It’s like a digital Frankenstein!
That's where ObjFiller-3D comes to the rescue! These researchers said, "Hold on, there's a better way!" They realized that instead of relying on individual 2D images, they could borrow techniques from video editing. Think about how video editing software can seamlessly remove objects from a scene or fill in missing frames. They adapted those techniques to work directly on 3D objects.
Now, you might be thinking: videos and 3D objects are totally different! And you'd be right. But the team figured out how to bridge that gap, cleverly adapting the video editing algorithms to understand and work with the 3D space. Imagine trying to translate a poem from English to Japanese - it is not a direct word for word translation, but rather understanding of the poem's intent and meaning. That's essentially what they did!
And here’s a cool twist: they also introduced a "reference-based" approach. So if you're trying to fix that broken vase handle, you could show the system a picture of a similar vase with a perfect handle. ObjFiller-3D can then use that reference to make a much better, more realistic repair. It's like having a skilled artisan guiding the computer!
"Instead of employing a conventional 2D image inpainting model, our approach leverages a curated selection of state-of-the-art video editing model to fill in the masked regions of 3D objects."
The results are pretty impressive. The researchers compared ObjFiller-3D to other methods, and it consistently produced more detailed and accurate reconstructions. They used some fancy metrics like PSNR and LPIPS, but basically, ObjFiller-3D's results looked way better to the human eye. They saw a PSNR of 26.6 compared to NeRFiller's 15.9 and a LPIPS of 0.19 compared to Instant3dit's 0.25!
Why does this matter?
This tech has the potential to revolutionize how we work with 3D objects, making it easier than ever to create, repair, and share them.
So, here are some things that are swirling around in my mind:
Definitely some food for thought! Check out the project page at https://objfiller3d.github.io/ and the code at https://github.com/objfiller3d/ObjFiller-3D and let me know what you think! Until next time, keep those neurons firing!
Credit to Paper authors: Haitang Feng, Jie Liu, Jie Tang, Gangshan Wu, Beiqi Chen, Jianhuang Lai, Guangcong Wang