arXiv:2511.16624v2 Announce Type: replace-cross Abstract: We present SAM 3D, a generative model for visually grounded 3D object reconstruction, predicting geometry, texture, and layout from a single image. SAM 3D excels in natural images, where occlusion and scene clutter are common and visual recognition cues from context play a larger role. We achieve this with a human- and model-in-the-loop pipeline for annotating object shape, texture, and pose, providing visually grounded 3D reconstruction data at unprecedented scale. We learn from this data in a modern, multi-stage training framework tha
Advances in generative AI models and efficient data annotation pipelines are enabling more sophisticated 3D reconstruction from 2D images.
This development significantly enhances capabilities for 3D content creation, essential for virtual reality, robotics, and digital twins, reducing reliance on manual modeling or specialized capture hardware.
The ability to rapidly '3Dfy' objects from single images democratizes 3D asset generation, broadens the foundation for AI's understanding of the physical world, and simplifies the creation of immersive digital environments.
- · Metaverse and VR/AR developers
- · Robotics and simulation industries
- · E-commerce (3D product visualization)
- · Gaming and entertainment sectors
- · Traditional 3D modeling services (for simple objects)
- · Companies reliant on expensive 3D scanning hardware
- · Photography-only visual recognition systems
Rapid generation of 3D assets will accelerate the development and adoption of virtual and augmented reality applications.
The increasing abundance of 3D data will enhance AI's spatial reasoning and physical world understanding, leading to more capable robots and autonomous systems.
The integration of real-world objects into digital environments will blur the lines between physical and virtual spaces, impacting digital ownership and interaction paradigms.
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Read at arXiv cs.AI