Segment Anything Model 2 (SAM 2) vs SAM 3D Objects
Compare Segment Anything Model 2 (SAM 2) and SAM 3D Objects side-by-side.
Compare Segment Anything Model 2 (SAM 2) vs SAM 3D Objects live
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These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
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Segment Anything Model 2 (SAM 2) vs SAM 3D Objects Comparison Table
Evals updated October 8, 2026Pricing updated October 8, 2026
| Property | Segment Anything Model 2 (SAM 2) | SAM 3D Objects |
|---|---|---|
| Organization | Meta | Meta |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Jul 2024 | Nov 2025 |
| Context Window | — | — |
| Parameters | 38.9M-224.4M | Unknown |
| License | Apache 2.0 | Custom |
| Vision Tasks | ||
| Instance Segmentation | Supported | Supported |
| 3D Reconstruction | Not listed | Supported |
| Model Features | ||
| Foundation Vision | Not listed | Supported |
Segment Anything Model 2 (SAM 2) vs SAM 3D Objects: Overview
SAM 2 is a real-time image and video segmentation model developed by Meta AI, released in July 2024 under the Apache 2.0 license. It extends the original Segment Anything Model to support video inputs by introducing a streaming memory architecture that maintains object state across frames, enabling consistent segmentation of objects through occlusion, motion, and scene changes. For image inputs, SAM 2 operates similarly to its predecessor with improved mask quality and speed.
SAM 2 accepts point, box, and mask prompts and produces object masks interactively or in a fully automated mode. Its memory architecture enables video segmentation at real-time speeds. SAM 2 is used in annotation pipelines, video analysis, robotic perception, and any application requiring high-quality promptable segmentation across both images and video.
SAM 3D Objects is a 3D reconstruction model released on November 19, 2025 by Meta AI as part of the broader SAM 3 release. It extends the Segment Anything Model family from 2D segmentation into 3D object reconstruction, predicting geometry, texture, and spatial layout for individual objects from a single RGB image. Given an image together with a prompt identifying the object (a segmentation mask, point, or bounding box), the model outputs a full textured 3D mesh, without requiring multi-view captures, depth sensors, or known camera parameters.
SAM 3D Objects uses a two-stage transformer architecture: a coarse stage that predicts 3D shape and object pose, followed by a refinement stage that adds texture and surface detail, with DINOv2 used to encode the input image. The model is trained via a human-and-model-in-the-loop data engine that combines synthetic 3D assets with real-image annotations, producing approximately 3.14 million mesh annotations across nearly 1 million images. Meta released SAM 3D Objects alongside SAM 3D Body, a companion model for single-image human mesh recovery, and SAM 3D Artist Objects (SA-3DAO), a new evaluation benchmark assembled with artist-created 3D ground truth. The model is designed as a companion to SAM 3 for downstream applications including augmented reality, robotics, content creation, and visual effects; it already powers the View in Room feature on Facebook Marketplace. SAM 3D Objects is released under the SAM 3 license; users should review the license terms prior to commercial use.