Summary of Lrm-zero: Training Large Reconstruction Models with Synthesized Data, by Desai Xie et al.
LRM-Zero: Training Large Reconstruction Models with Synthesized Data
by Desai Xie, Sai Bi, Zhixin Shu, Kai Zhang, Zexiang Xu, Yi Zhou, Sören Pirk, Arie Kaufman, Xin Sun, Hao Tan
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents LRM-Zero, a Large Reconstruction Model trained entirely on synthesized 3D data. The model is trained using the procedural 3D dataset Zeroverse, which is automatically generated from simple primitive shapes with random texturing and augmentations. Unlike previous datasets captured or crafted by humans, Zeroverse ignores realistic global semantics but features complex geometric and texture details similar to real objects. LRM-Zero achieves high-quality sparse-view 3D reconstruction, competitive with models trained on Objaverse. The paper analyzes critical design choices of Zeroverse contributing to training stability. This work demonstrates that 3D reconstruction can be addressed without realistic object semantics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a Large Reconstruction Model called LRM-Zero that uses fake 3D data to learn how to rebuild real-world objects from a few viewpoints. They made this fake data by combining simple shapes in different ways and adding random textures and details. This allows the model to focus on learning the underlying patterns and shapes, rather than trying to recognize specific objects or scenes. The model is able to build 3D reconstructions of real objects that are just as good as models trained on real data. |
Keywords
* Artificial intelligence * Semantics
