SAM 3: The Eyes for AI — Nikhila & Pengchuan (Meta Superintelligence), ft. Joseph Nelson (Roboflow)
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What is SAM 3 and how does it differ from SAM 1 and SAM 2?
Okay, we're here in the remote studio with the grand return of the Roboflow and Latin Space and Sam Combo. Uh welcome to Joseph, my sort of uh vision co-host, I guess. Thanks. Great to be here. Welcome back. We also have welcome back Nikola Ravi, who's uh the lead on Sam two I guess just Sam in general, right? Um and we have uh jo joining us Peng Chuan, uh who is also a researcher on Sam. Yeah, nice to So congrats on Sam 3's launch. I mean, like the the demo each time you step it up like really amazingly. And I think like every time my my general impression or or takeaway when I tell people about Sam is like just the uh every time you have a new release, like it's like once a year you show up, you drop a banger and then you you like you know you just like drop the mic and and and and go go for next year.
And you also add a dimension. So I was entirely Like we're really not surprised when Sam three had the three D thing. 'Cause I'm like, Well yeah, which which is the next dimension to go? It's like three D. Like
Yeah, actually maybe just on that, w I think that's actually a common misconception. We launched actually three separate models this time. It was SAM three SAM three D objects and SAM three D body.
Yes.
Those were two completely separate models and SAM three is just the image and video understanding model.
Which is on a on a debtor backbone and is sped up. Yeah, I sorry, I didn't I didn't mean to uh sort of pre preface all all all this. But maybe for just to remind our audience or maybe uh for it for for people in new to the Sam series of a podcast that we've done so far, maybe each of you can sort of go around and uh intro like your or your sort of entry into computer vision or sort of your relationship with Sam. Uh go ahead, Nikki.
Okay, cool. Hi everyone. I'm Nikila. I'm a researcher at Meta. I've been at Meta for eight and a half years. So really been through m evolution of the field, um, in that time. It really started working on A range of different problems in computer vision, worked briefly on 3D. We've got this library called PyTorch 3D. But really started on this segment anything as a project in around sort of late 2021. So it's actually, you know, been almost four years since I've been like working on this segment anything space. And you know, we started with Sam One in twenty twenty-three, Sam two last year in July twenty twenty four, and then now SAM three. So it's been, you know, a culmination of a lot of work of a lot of people over the years.
So yeah, really, really excited to be at this point and you know, get to share it with all of you. Um I'll hand it over to you, Penkton.
Hello everyone, so I'm Peng Chuan. I'm a researcher at Cesan Team. I have been working in computer vision in this field for nearly nine years, starting from twenty seventeen. Kinda I think it's a long time. I have been working in uh MSR for five years and then kinda moved to Meta Reality Lab to work on uh egocentric foundation models, uh on AI glasses for a while and then In 2023, I moved to Sun Team and that time is exactly the start time of Saint Slui and running and I think that's the knife time experience I have on the Sens Lui team. And it's glad that Saint Slui is out and I kind of achieved my original grand goal of computer vision to reach kind of human performance of detection, segmentation, tracking, image and videos.
I'm Joseph co-founder.
Where our mission is to make the world programmable. We think software should have the sense of sight, and models like SAM and others are critical to unlocking that capability. Now, millions of developers, half the Fortune 100, build with Roboflow's tools and infrastructure to create and deploy models to production. We've been big believers of the meta family of open source models all the way back to like mask RCNN and Detectron 2. All the way to present of SAM 1, SAM2, and SAM 3. The work that the Meta team does to advance state-of-the-art and open source computer vision has been bedrock to enabling developers and enterprises globally to adopt AI.
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Chapters
8 chapters
1
What is SAM 3 and how does it differ from SAM 1 and SAM 2?
0:03–9:05
2
How do concept prompts enable exhaustive, zero‑click segmentation and tracking?
9:05–19:07
3
What is the SACO benchmark and why does it matter for open‑vocabulary detection?
19:07–27:52
4
How does the SAM 3 data engine automate annotation from minutes to seconds?
27:52–36:25
5
Why does SAM 3 separate recognition (presence token) from localization?
36:25–45:51
6
How does decoupling the detector and tracker improve video identity preservation?
45:51–55:33
7
What are SAM 3 Agents and how do they enable visual reasoning with LLMs?
55:33–1:06:22
8
What real‑world impact has SAM 3 had on labeling efficiency and downstream applications?
1:06:22–1:14:50
Speakers
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