Meta Segment Anything Model 3

[trevorhlynn]

This was front page for a while last week

https://news.ycombinator.com/item?id=45982073

[dang]

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.

[stronglikedan]

what is old is new again

[vessenes]

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 under 15 seconds.

[Qwuke]

Between this and DINOv3, Meta is doing a lot for the SOTA even if Llama 4 came up short compared to the Chinese models.

[nl]

https://ai.meta.com/blog/sam-3d/ for those interested.

[Fraterkes]

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

[visioninmyblood]

you can download them at https://github.com/facebookresearch/sam3. for 3d https://github.com/facebookresearch/sam-3d-objects

[retinaros]

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

[enoch2090]

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

[zubiaur]

Had good luck with Gemini 2.5, SAM3 failed miserably with PIDs.

[retinaros]

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

[the_duke]

Side question: what are the current top goto open models for image captioning and building image embeddings dbs, with somewhat reasonable hardware requirements?

[daemonologist]

For pure image embedding, I find DINOv3 to be quite good. For multimodal embedding, maybe RzenEmbed. For captioning I would use a regular multimodal LLM, Qwen 3 or Gemma 3 or something, if your compute budget allows.

[NitpickLawyer]

Try any of the qwen3-vl models. They have 8, 4 and 2B models in this family.

[Glemkloksdjf]

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.

[phkahler]

Which (if any) of these models could run on a RaspberryPi for object recognition at several FPS?

[aliljet]

I wonder how effective this is medical scenarios? Segmenting organs and tumors in cat scans or MRIs?

[colkassad]

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

[observationist]

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.

[knicholes]

I was approved within about 10 minutes for "Segment Anything 3"

[tschellenbach]

same here, didn't get approval

[shashanoid]

Miss the old segment anything page, used it a lot. This UI I found very complex to use

[bradyriddle]

Same.

Checkout https://github.com/MiscellaneousStuff/meta-sam-demo

It's a rip of the previous sam playground. I use it for a bunch of things.

Sam 3 is incredible. I'm surprised it's not getting more attention.

[vanjoe]

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.

[cheesecompiler]

This would be convenient for post-production and editing of video, e.g. to aid colour grading in Davinci Resolve. Currently a lot of manual labour goes into tracking and hand-masking in grading.

[maelito]

I wonder if this can be used to track an object's speed. E.g. a vehicle on a road. It would need to recognize shapes, e.g. car model or average size of a bike, to guess a speed.

[Workaccount2]

I do a test on multimodal LLMs where I show them a dog with 5 legs, and ask them to count how many legs the dog has. So far none of them can do it. They all say "4 legs".

Segment anything however was able to segment all 5 dog legs when prompted to. Which means that meta is doing something else under the hood here, and may lend itself to a very powerful future LLM.

Right now some of the biggest complaints people have with LLMs stems from their incompetence processing visual data. Maybe meta is onto something here.

[jampekka]

Segmentation doesn't need to count legs. I'd guess something like YOLO could segment 5 legged dogs too.

[nerdsniper]

You don’t need segmentation to count legs. Object detection can do that. DeepLabCut from 2020 perhaps.

[PunchTornado]

I doubt that gemini 3 cannot do it.