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LRM: Large Reconstruction Model for Single Image to 3D

Yicong Hong · Kai Zhang · Jiuxiang Gu · Sai Bi · Yang Zhou · Difan Liu · Feng Liu · Kalyan Sunkavalli · Trung Bui · Hao Tan

Halle A 7
[ ] [ Visit Oral 2B ]
Tue 7 May 7:15 a.m. — 7:30 a.m. PDT


We propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds. In contrast to many previous methods that are trained on small-scale datasets such as ShapeNet in a category-specific fashion, LRM adopts a highly scalable transformer-based architecture with 500 million learnable parameters to directly predict a neural radiance field (NeRF) from the input image. We train our model in an end-to-end manner on massive multi-view data containing around 1 million objects, including both synthetic renderings from Objaverse and real captures from MVImgNet. This combination of a high-capacity model and large-scale training data empowers our model to be highly generalizable and produce high-quality 3D reconstructions from various testing inputs, including real-world in-the-wild captures and images created by generative models. Video demos and interactable 3D meshes can be found on our LRM project webpage:

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