Meta: SAM 3D Objects

SAM 3D Objects Overview

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.

SAM 3D Objects Details & Performance

Details

Resources

Vision Tasks

Instance Segmentation3D Reconstruction

Features

Foundation Vision

Usage

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Performance

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Released on November 19th, 2025, Segment Anything 3 (SAM 3) is a zero-shot image segmentation model that “detects, segments, and tracks objects in images and videos based on concept prompts.” This model was developed by Meta as the third model in the Segment Anything series.

Unlike its previous SAM models (Segment Anything and Segment Anything 2), you can provide SAM 3 with the prompt “shipping container” and it will generate precise segmentation masks for all shipping containers in an image. SAM 3 generates segmentation masks that correspond to the location of the objects found with a text prompt.

Segment Anything Model 2 (SAM 2)

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.

Depth Anything V2

Depth Anything V2 is a monocular depth estimation model released in June 2024 by researchers at the University of Hong Kong and TikTok. It predicts a dense depth map from a single RGB image, enabling 3D-aware applications without the need for stereo cameras, LiDAR, or multi-view inputs. The model improves on the original Depth Anything through three modifications: replacing real labeled images with 595K high-quality synthetic images during teacher training, scaling up teacher model capacity, and using the stronger teacher to generate pseudo-labels on 62 million unlabeled real images used to train the student models. This pipeline reduces the depth prediction artifacts that can occur in reflective, transparent, and texture-poor regions. Compared to diffusion-based depth models such as Marigold, Depth Anything V2 runs more than 10× faster while producing more accurate predictions.Depth Anything V2 is released in four sizes: Small (25M), Base (97M), Large (335M), and Giant (1.3B), and in two output modes: relative depth (normalized scene-level estimates) and metric depth (absolute distance in meters, produced by fine-tuning the relative-depth backbone on depth-annotated datasets). The Small, Base, and Large model weights are released under Apache 2.0, and the Giant variant under CC-BY-NC-4.0 for non-commercial use. A successor model, Depth Anything 3, was released in November 2025 by the ByteDance Seed team, extending the framework to multi-view depth estimation and camera pose recovery.

SAM-CLIP is a unified vision foundation model introduced by researchers at Apple and the University of Illinois Urbana-Champaign in October 2023. It merges two popular vision foundation models — Meta's Segment Anything Model (SAM) and OpenAI's CLIP — into a single shared Vision Transformer backbone through a combination of multi-task learning, continual learning, and teacher-student distillation. The method requires only a small fraction of the original pretraining datasets and demonstrates that complementary capabilities from distinct foundation models can be consolidated without retraining from scratch, reducing the storage and compute cost of running both models in inference.The resulting model retains SAM's zero-shot segmentation ability and CLIP's zero-shot classification and image-text retrieval, while introducing new capabilities the individual models lacked. SAM-CLIP establishes state-of-the-art results on zero-shot semantic segmentation across five benchmarks, improving mean IoU by 6.8 points on Pascal VOC and 5.9 points on COCO-Stuff over prior specialized models. The paper was accepted at the UniReps Workshop at NeurIPS 2023 and the eLVM Workshop at CVPR 2024. Apple has published the research but has not released model weights or inference code publicly.

SAM 3D Objects License

Custom License · Model-specific license

SAM 3D Objects ships under a custom, model-specific license rather than a standard permissive or restrictive one, so the SAM 3D Objects license has to be read directly. Custom model licenses range from effectively permissive to research-only.

Commercial use: Varies. Custom model licenses commonly restrict commercial use, cap monthly active users, or carve out specific industries — check the SAM 3D Objects terms before you build on it.
Modification: Usually permitted for fine-tuning, but check whether derivative weights inherit the same license and naming requirements.
Redistribution: Often restricted. Look for attribution, naming, and acceptable-use requirements that apply to any copy you share.

Uncertainty around licensing can delay or stop a project, and acceptable-use policies attached to custom licenses are binding terms rather than guidance. Review them alongside the SAM 3D Objects license before production deployment.

Do I need a commercial license for SAM 3D Objects?

If the custom terms rule out your use case, a commercial license from the rights holder is the way through. Roboflow's licensing page lists the supported models whose commercial license is included in a Roboflow plan, so it is worth checking whether SAM 3D Objects — or a permissively licensed alternative — fits your deployment.

Do not hesitate to reach out with questions for your commercial project — our team will help you start solving business problems on the first call. See Roboflow commercial licensing for the models included in each plan.

This model is released under a custom license that does not match a standard open-source identifier. Read the full license text linked from the model documentation.

Custom licenses vary widely in what they permit. Many model-specific custom licenses include commercial-use restrictions (e.g., non-commercial weights, named-user limits, or jurisdiction restrictions). Read the full license before deploying commercially.

Custom licenses are model-specific. Always check the per-model License Notes section above and the linked official license text.

License information is provided as a guide and is not legal advice.