Capture Gaussian splats of Christmas cookies: https://superspl.at/view?id=bd964899
Readit Newsdanybittel commented on Ask HN: What Are You Working On? (December 2025) · Posted by u/david927
This says it all:
> I haven’t met anyone who doesn’t believe artificial intelligence has the potential to be one of the biggest technological developments of all time, reshaping both daily life and the global economy.
You’re trying to weigh in on this topic and you didn’t even _talk_ to a bear?
From the article:
...AI is currently the subject of great enthusiasm. If that enthusiasm doesn’t produce a bubble conforming to the historical pattern, that will be a first.
If you want to see a mosquito and it's proboscis up close, I recently scanned one into a gaussian splat: https://superspl.at/view?id=b4cbf5d6
This sounds a lot like Learned helplessness: https://en.wikipedia.org/wiki/Learned_helplessness
Pinhole lens + high light/long exposures to get sharp focus may help avoid some of the extra processing steps, he does mention he shot small aperture and that can cause diffraction effects and I guess that might be worse with pinhole though.
It all kind of depends on each other. More light, means longer recycle times on the speedlights or higher iso, more noise. Longer exposure isn't an option with speedlights, using continuous also has it's downsides, things may start to shake..
The linked paper describes a pipeline that starts with “point cloud from SfM” so they’re assuming away this problem at the moment.
Is it possible to handle SfM out of band? For example, by precisely measuring the location and orientation of the camera?
The paper’s pipeline includes a stage that identifies the in-focus area of an image. Perhaps you could use that to partition the input images. Exclusively use the in-focus areas for SfM, perhaps supplemented by out of band POV information, then leverage the whole image for training the splat.
Overall this seems like a slow journey to building end-to-end model pipelines. We’ve seen that in a few other domains, such as translation. It’s interesting to see when specialized algorithms are appropriate and when a unified neural pipeline works better. I think the main determinant is how much benefit there is to sharing information between stages.
You can definitely feed camera intrinsic (lens, sensor size..) and extrinsic (position, rotation..) into the SfM. While the intrinsic are very useful the extrinsic not actually that much. In no way can you measure the rotation good enough, to get subpixel accuracy. The position can be useful as an initial guess, but I found it more hassle than worth it. If the images track well, have enough overlap, you can get exact tracking out of them without dealing with extrinsic. If they don't track well, extrinsic won't save you. That was at least my experience.
Cool! It looks awesome. I did see some "ghost legs" on the bumblebee. How does that sort of artifact happen?
The bumblebee was my first attempt, the tracking didn't quite work, so you get ghosting. Others too have ghosting, usually happens when part of the insect moves, while shooting (which takes 4h). They dry and crumble after a while.
Really amazing results.
I wonder if one could capture each angle in a single shot with a Lytro Illum instead of focus-stacking? Or is the output of an Illum not of sufficient resolution?
That would be awesome if it worked, from a curious look I can't say why not. I'll have to investigate a bit more. Thanks for bringing it up.
Love it!
https://superspl.at/view?id=ac0acb0e
I believe this one is misnamed
Thanks for pointing that out, fixed it.
Multiple groups working on this:
Thanks for the links, that is great to know.
I'm not quite sold if it's the better approach. You'd need to do SfM (tracking) on the out of focus images, which with macro subject can be really blurry, I don't know how well that works.. and a lot more of images too. You'd have group them somehow or preprocess.. then you're back to focus stacking first :-)