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Meta Segment Anything Model 3

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Re: Meta Segment Anything Model 3

#41

This was front page for a while last week https://news.ycombinator.com/item?id=45982073

Thanks! Macroexpanded:

Meta Segment Anything Model 3 - https://news.ycombinator.com/item?id=45982073 - Nov 2025 (133 comments)

p.s. This was lobbed onto the frontpage by the second-chance pool (https://news.ycombinator.com/item?id=26998308) and I need to make sure we don't end up with duplicate threads that way.

Re: Meta Segment Anything Model 3

#42

Released last week. Looks like all the weights are now out and published. Don’t sleep on the SAM 3D series — it’s seriously impressive. They have a human pose model which actually rigs and keeps multiple humans in a scene with objects, all from one 2D photo (!), and their straight object 3D model is by far the best I’ve played with - it got a really very good lamp with translucency and woven gems in usable shape in u…

I looked quickly but it does not generate a 3d model file right?

Re: Meta Segment Anything Model 3

#44
For a long time I've wanted to use something like this to remove advertisements from hockey games.The moving ads on the boards are really annoying. Maybe I'll get around to actually doing that one of these days.

Re: Meta Segment Anything Model 3

#46

Released last week. Looks like all the weights are now out and published. Don’t sleep on the SAM 3D series — it’s seriously impressive. They have a human pose model which actually rigs and keeps multiple humans in a scene with objects, all from one 2D photo (!), and their straight object 3D model is by far the best I’ve played with - it got a really very good lamp with translucency and woven gems in usable shape in u…

Are those the actual wireframes they're showing in the demos on that page? As in, do the produced models have "normal" topology? Or are they still just kinda blobby with a ton of polygons

I’ve only used the playground. But I think they are actual meshes - they don’t have any of the weird splat noise at the edge of the objects, and they do not seem to show similar lighting artifacts to a typical splat rendering.

Re: Meta Segment Anything Model 3

#47
post #21

Earlier quoted context omitted.

I would suggest YOLO. Depending on your domain, you might also finetune these models. Its relativly easy as they are not big LLMs but either image classification or bounding boxes. I would recommend bounding boxes.

What do you mean "bounding boxes"? They were talking about captions and embeddings, so a vision language model is required.

I suggested YOLO and non llm-vl as a lot faster alternative.

Of course CLIP would be otherwise the other option than a big llm-vl one.

Re: Meta Segment Anything Model 3

#48

Earlier quoted context omitted.

Any current one. they are easy to use and you can just benchmark them yourself. I'm using small and medum. Also the code for using it is very short and easy to use. You can also use ChatGPT to generate small exepriments to see what fits your case better

There aren’t any YOLO models for captioning and the other models aren’t robust enough to make for good embedding models.

You can get labels out of the classifier and bounding box models.

They are super fast.

Its just an alternative i'm mentioning. I would assume a person knowing a little bit of that domain.

Otherwise the first option would be CLIP i assume. llm-vl is just super slow and compute intensive.

Re: Meta Segment Anything Model 3

#49

Been waiting days to get approval to download this from huggingface. What's up with that?

Alternative downloads exist. You can find torrents, and match checksums against the HF downloads, but there are also mirrors and clones right there in HF which you can download without even having to log in.

Thanks, got it and it's working wonders for my use case.

Re: Meta Segment Anything Model 3

#50

Surprisingly, SAM3 works bad on engineering drawings while SAM2 kinda works, and VLMs like Qwen3-VL works as well

yeah I tried too. Im trying a fine tuning on PIDs.

Looking forward to your progress! Just checked the paper and it says the underlying backbone is still DETR. My guess would be that SAM3 uses more video frames during the training process and caused the dilution of sparse engineering-paper-like data.
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