sam3d
sam3d is Meta’s single-image 3D reconstruction model that fuses SAM 3 open-vocabulary segmentation with geometry, texture, and layout predictions—turning ordinary photos into ready-to-use 3D assets.
What is sam3d
sam3d is Meta’s research-grade model that reconstructs 3D geometry, texture, and layout from just one image. It extends the Segment Anything family, enabling text or visual prompts to isolate objects and quickly convert them into 3D assets.
- Delivers geometry, texture, and spatial layout from a single RGB frame.
- Integrates seamlessly with SAM 3 open-vocabulary segmentation for targeted reconstructions.
- Backed by open-source checkpoints, inference code, and curated datasets like Artist Objects.
- Designed for creative tools, AR commerce, robotics perception, and scientific visualization.
Features of sam3d
Core differentiatorssam3d single-image → 3D
Infers full 3D shape, texture, and layout from one RGB photo—replacing multi-view and LiDAR setups in many workflows.
sam3d open-vocabulary
Uses SAM 3 prompts (text, points, boxes) to isolate objects and deliver targeted 3D assets from natural language or visual cues.
sam3d open ecosystem
Ships checkpoints, inference code, and benchmarks like Artist Objects and SAM 3D Body for reproducible research and production pilots.
sam3d XR ready
Feeds AR/VR pipelines: import single-image scans into virtual rooms, mixed reality scenes, and immersive storytelling.
sam3d efficient inputs
Reduces capture complexity: works with legacy photos, user-generated content, and single product shots.
sam3d benchmarks
Includes clear evaluation suites so teams can measure performance, identify domain gaps, and fine-tune where needed.
sam3d vs traditional 3D capture
Decision table| Criteria | Traditional multi-view / LiDAR | sam3d single-image |
|---|---|---|
| Input | Many calibrated views or depth sensor | One RGB photo with optional SAM 3 prompt |
| Setup | Controlled rigs, careful lighting | Everyday devices; works with existing images |
| Speed | Long capture + processing loops | Rapid reconstruction, near instant previews |
| Coverage | Limited for single-angle or historical photos | Works on single-angle, legacy, or user posts |
| Integration | Custom pipelines, heavier preprocessing | Pairs with SAM 3; plug inference code & checkpoints |
| Weak spots | Costly, complex capture; not mobile-ready | Sensitive to low quality images; extreme tails |
sam3d use cases
Impact zonesCreative production
Accelerate games, CGI, and social content by scanning products or props from a single photo and refining in Blender or game engines.
E-commerce & AR shopping
Enable “view in room” with one product shot; use SAM 3 to segment, sam3d to reconstruct, and render instantly in AR viewers.
Robotics & autonomy
Provide 3D priors when depth is missing; infer shape and free space from cameras to complement LiDAR perception stacks.
Medical & scientific viz
Turn 2D scans or microscopy into 3D forms for inspection; fine-tune sam3d for anatomy, biology, or lab domains.
How to use sam3d
Hands-on stepsCapture & prompt
Use a single, well-lit RGB image. Optionally apply SAM 3 with a text or box prompt to isolate the target object.
Reconstruct with sam3d
Run inference using the released checkpoints and code; sam3d predicts geometry, texture, and layout directly.
Export & deploy
Export the mesh/texture; place into AR viewers, 3D engines, robotics simulators, or marketing experiences.
- Sharp images, balanced lighting, minimal occlusion.
- Simple backgrounds improve mask quality and geometry.
- Use SAM 3 prompts to isolate the exact object of interest.
- Benchmark on your own data; fine-tune for domain specifics.
- Measure latency and cost for interactive AR/VR scenarios.
sam3d strengths & limits
ExpectationsStrengths
- Single-image capture reduces hardware and setup complexity.
- Open-source checkpoints, inference code, and benchmarks accelerate adoption.
- Pairs with SAM 3 for text-to-3D object extraction.
- Works on legacy or user-generated images for rapid AR/VR workflows.
Limitations
- Sensitive to low-quality images, extreme lighting, or heavy occlusions.
- Rare object categories and fine-grained structures may be misinterpreted.
- Dynamic humans or deformable objects remain challenging.
- Latency and compute must be profiled for production scale.
sam3d FAQ
10 answersWhat makes sam3d different from photogrammetry?
sam3d reconstructs from a single image with SAM 3 prompts, while photogrammetry needs many calibrated views and controlled capture.
Does sam3d need depth sensors?
No. sam3d predicts geometry, texture, and layout from RGB alone, reducing reliance on LiDAR or depth cameras.
Can sam3d isolate specific objects?
Yes. Use SAM 3 open-vocabulary prompts (text, points, boxes) to mask the object, then pass it to sam3d for clean reconstruction.
Where does sam3d struggle?
Low-res, noisy, or heavily occluded images; rare categories; deformable humans; and scenes with motion blur.
Is sam3d open-source?
Meta provides checkpoints, inference code, and benchmarks, enabling reproducible research and production pilots.
Which datasets ship with sam3d?
Benchmarks include the artist-curated Artist Objects set and SAM 3D Body data for human mesh recovery.
How does sam3d help AR commerce?
Convert single product shots into AR-ready 3D assets, powering “view in room” with minimal capture overhead.
Can sam3d run in real time?
Latency depends on hardware and optimization (batching, quantization, distillation). Profile for your target devices.
How do I improve sam3d output?
Capture sharp images, simplify backgrounds, and fine-tune on domain-specific data to reduce artifacts.
What about safety and privacy?
Establish consent and policies for reconstructing private spaces; consider watermarking or usage controls for sensitive deployments.
sam3d official resources
ExternalMeta’s overview of the sam3d model, demos, and entry points.
Meta AI blog detailing single-image reconstruction and benchmarks.
Official code & weights for object reconstruction.
Research publication covering architecture, benchmarks, and results.
Build with sam3d today
Download checkpoints, run inference on your own imagery, and bring single-image 3D reconstruction to AR/VR viewers, e-commerce, or robotics stacks.
sam3d trust & EEAT signals
Updated 2025-11-20Content curated by the sam3d editorial team with citations to Meta AI sources and official GitHub repositories.
Key claims reference Meta AI blog, research papers, and sam3d GitHub code to keep guidance verifiable and current.
Encourages consent-aware 3D capture, privacy-safe reconstruction, and benchmarking on your own data before deployment.