SAM 3D: 3Dfy Anything in Images
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.
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SAM 3D Team
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Xingyu Chen
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Fu-Jen Chu
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Pierre Gleize
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Pierre Gleize
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Kevin J Liang
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Kevin J Liang
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Alexander Sax
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Hao Tang
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Weiyao Wang
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Michelle Guo
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Thibaut Hardin
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Thibaut Hardin
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Xiang Li
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Aohan Lin
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Jiawei Liu
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Ziqi Ma
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Anushka Sagar
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Anushka Sagar
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Bowen Song
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Xiaodong Wang
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Jianing Yang
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Bowen Zhang
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Bowen Zhang
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Piotr Dollár
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Georgia Gkioxari
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Matt Feiszli
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Matt Feiszli
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Jitendra Malik
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Jitendra Malik 
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SAM 3D: 3Dfy Anything in Images
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.
SAM 3D is a generative model that reconstructs 3D objects from single images using a multi-stage training framework that includes synthetic pretraining and real-world alignment, achieving high performance in human preference tests.
generative model 3D object reconstruction geometry texture layout human-in-the-loop object shape object pose multi-stage training synthetic pretraining real-world alignment 3D data barrier human preference tests benchmark