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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

Run the same image across every model that supports a task and compare their outputs side-by-side.

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 (SAM) vs SAM 3D Objects Comparison Table

Evals updated October 8, 2026Pricing updated October 9, 2026

PropertySegment Anything Model (SAM)SAM 3D Objects
OrganizationMetaMeta
Categoryopenopen
Modalityvisionvision
Release DateApr 2023Nov 2025
Context Window——
Parameters91M-636MUnknown
LicenseApache 2.0Custom
Vision Tasks
Instance SegmentationSupportedSupported
3D ReconstructionNot listedSupported
Model Features
Foundation VisionSupportedSupported

Segment Anything Model (SAM) vs SAM 3D Objects: Overview

Segment Anything Model (SAM)

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

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.

MetaSegment Anything Model (SAM)