SAM 3D: 3Dfy Anything in Images
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Abstract (arXiv)
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 that combines synthetic pretraining with real-world alignment, breaking the 3D "data barrier". We obtain significant gains over recent work, with at least a 5:1 win rate in human preference tests on real-world objects and scenes. We will release our code and model weights, an online demo, and a new challenging benchmark for in-the-wild 3D object reconstruction.
Key facts. Cited by 230 papers in the map (data through 7 Oct 2026). Builds on 93 of its 126 references that are in the map. First posted on arXiv 20 Nov 2025.
Builds on
93 of 126 references.
References · 93
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50 most cited of 93 All 93 (JSON)
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Citing papers per year: 2025: 9; 2026: 221.
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