COMPUTER VISION
SAM 3D: 3Dfy Anything in Images
November 19, 2025
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.
AUTHORS
Written by
SAM 3D Team
Jianing Yang
Georgia Gkioxari
Anushka Sagar
Aohan Lin
Bowen Song
Bowen Zhang
Fu-Jen Chu
Hao Tang
Jiawei Liu
Jitendra Malik
Matt Feiszli
Michelle Guo
Pierre Gleize
Alexander Sax
Thibaut Hardin
Weiyao Wang
Xiang Li
Xiaodong Wang
Xingyu Chen
Ziqi Ma
Publisher
arXiv
Research Topics
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