SAM 3D: 3Dfy Anything in Images
Authors: name: SAM 3D Team; name: Xingyu Chen; name: Fu-Jen Chu; name: Pierre Gleize; name: Kevin J Liang; name: Alexander Sax; name: Hao Tang; name: Weiyao Wang; name: Michelle Guo; name: Thibaut Hardin; name: Xiang Li; name: Aohan Lin; name: Jiawei Liu; name: Ziqi Ma; name: Anushka Sagar; name: Bowen Song; name: Xiaodong Wang; name: Jianing Yang; name: Bowen Zhang; name: Piotr Dollár; name: Georgia Gkioxari; name: Matt Feiszli; name: Jitendra Malik
Published: 2025-11-20 18:31:46

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 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.