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
Organization: AI at Meta

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.