LRM: Large Reconstruction Model for Single Image to 3D
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 from generative models. Video demos
and interactable 3D meshes can be found on this website:
https://yiconghong.me/LRM/.
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Yicong Hong
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2023-11-09T09:33:52.791Z
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Yicong Hong
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Kai Zhang
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Kai Zhang
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Jiuxiang Gu
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2023-11-10T07:58:28.519Z
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Jiuxiang Gu
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Sai Bi
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Yang Zhou
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Difan Liu
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2023-11-09T09:07:30.782Z
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Difan Liu
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Feng Liu
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Kalyan Sunkavalli
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Trung Bui
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2023-11-09T09:48:12.552Z
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Trung Bui
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Trung
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Hao Tan
LRM: Large Reconstruction Model for Single Image to 3D
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 from generative models. Video demos
and interactable 3D meshes can be found on this website:
https://yiconghong.me/LRM/.
A Large Reconstruction Model using a transformer-based architecture predicts 3D neural radiance fields from single images using massive multi-view training data.
Large Reconstruction Model transformer-based architecture neural radiance field ShapeNet Objaverse MVImgNet end-to-end training