Real3D: Scaling Up Large Reconstruction Models with Real-World Images
The default strategy for training single-view Large Reconstruction Models
(LRMs) follows the fully supervised route using large-scale datasets of
synthetic 3D assets or multi-view captures. Although these resources simplify
the training procedure, they are hard to scale up beyond the existing datasets
and they are not necessarily representative of the real distribution of object
shapes. To address these limitations, in this paper, we introduce Real3D, the
first LRM system that can be trained using single-view real-world images.
Real3D introduces a novel self-training framework that can benefit from both
the existing synthetic data and diverse single-view real images. We propose two
unsupervised losses that allow us to supervise LRMs at the pixel- and
semantic-level, even for training examples without ground-truth 3D or novel
views. To further improve performance and scale up the image data, we develop
an automatic data curation approach to collect high-quality examples from
in-the-wild images. Our experiments show that Real3D consistently outperforms
prior work in four diverse evaluation settings that include real and synthetic
data, as well as both in-domain and out-of-domain shapes. Code and model can be
found here: https://hwjiang1510.github.io/Real3D/
666b0dd1a10c1e31ff37e704
Hanwen Jiang
admin_assigned
2024-06-14T11:26:03.683Z
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/avatars/462841d3ba16d351182e9aa552c36044.svg
Hanwen Jiang
user
hwjiang
666b0dd1a10c1e31ff37e705
Qixing Huang
claimed_verified
2025-04-08T07:01:28.638Z
67f18d051358145ee5bb3a11
/avatars/e5a8c12295d7609625c2b891d47c3112.svg
qixing huang
user
qixinghuang1
666b0dd1a10c1e31ff37e706
Georgios Pavlakos
admin_assigned
2024-06-14T11:26:29.655Z
6478d3433b7f8b1f6249b469
/avatars/11e7d7a94ae26500c1c2ad62e760726f.svg
Georgios Pavlakos
user
geopavlakos
Real3D: Scaling Up Large Reconstruction Models with Real-World Images
The default strategy for training single-view Large Reconstruction Models
(LRMs) follows the fully supervised route using large-scale datasets of
synthetic 3D assets or multi-view captures. Although these resources simplify
the training procedure, they are hard to scale up beyond the existing datasets
and they are not necessarily representative of the real distribution of object
shapes. To address these limitations, in this paper, we introduce Real3D, the
first LRM system that can be trained using single-view real-world images.
Real3D introduces a novel self-training framework that can benefit from both
the existing synthetic data and diverse single-view real images. We propose two
unsupervised losses that allow us to supervise LRMs at the pixel- and
semantic-level, even for training examples without ground-truth 3D or novel
views. To further improve performance and scale up the image data, we develop
an automatic data curation approach to collect high-quality examples from
in-the-wild images. Our experiments show that Real3D consistently outperforms
prior work in four diverse evaluation settings that include real and synthetic
data, as well as both in-domain and out-of-domain shapes. Code and model can be
found here: https://hwjiang1510.github.io/Real3D/
Real3D is an unsupervised LRM training framework that leverages both synthetic and real single-view images to improve performance across various evaluation settings.
Large Reconstruction Models LRM Real3D self-training framework unsupervised losses pixel-level semantic-level automatic data curation in-the-wild images