Segment Anything Model (SAM) vs SAM 3D Objects
Compare Segment Anything Model (SAM) and SAM 3D Objects side-by-side.
Compare Segment Anything Model (SAM) vs SAM 3D Objects live
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Models in this comparison
Segment Anything Model (SAM) vs SAM 3D Objects Comparison Table
Evals updated October 8, 2026Pricing updated October 9, 2026
| Property | Segment Anything Model (SAM) | SAM 3D Objects |
|---|---|---|
| Organization | Meta | Meta |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Apr 2023 | Nov 2025 |
| Context Window | — | — |
| Parameters | 91M-636M | Unknown |
| License | Apache 2.0 | Custom |
| Vision Tasks | ||
| Instance Segmentation | Supported | Supported |
| 3D Reconstruction | Not listed | Supported |
| Model Features | ||
| Foundation Vision | Supported | Supported |
Segment Anything Model (SAM) vs SAM 3D Objects: Overview
The Segment Anything Model is a promptable image segmentation foundation model developed by Meta AI, released in April 2023 under the Apache 2.0 license. It introduces a general-purpose segmentation architecture trained on SA-1B, a dataset of over 1 billion masks across 11 million images collected using a data engine that leveraged the model itself. SAM accepts point, bounding box, and mask prompts and generates high-quality segmentation masks for any object in an image, including objects not seen during training.
SAM achieves strong zero-shot performance across a wide range of segmentation tasks and domains. Its promptable interface makes it suitable as a building block for automated annotation, interactive segmentation tools, and integration with detection models such as Grounding DINO. SAM has been extended by subsequent works including SAM 2, SAM 3, and Grounded-SAM.
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.