GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement
<|reference_start|>GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement: We propose a novel approach for 3D mesh reconstruction from multi-view images. Our method takes inspiration from large reconstruction models like LRM that use a transformer-based triplane generator and a Neural Radiance Field (NeRF) model trained on multi-view images. However, in our method, we introduce several important modifications that allow us to significantly enhance 3D reconstruction quality. First of all, we examine the original LRM architecture and find several shortcomings. Subsequently, we introduce respective modifications to the LRM architecture, which lead to improved multi-view image representation and more computationally efficient training. Second, in order to improve geometry reconstruction and enable supervision at full image resolution, we extract meshes from the NeRF field in a differentiable manner and fine-tune the NeRF model through mesh rendering. These modifications allow us to achieve state-of-the-art performance on both 2D and 3D evaluation metrics, such as a PSNR of 28.67 on Google Scanned Objects (GSO) dataset. Despite these superior results, our feed-forward model still struggles to reconstruct complex textures, such as text and portraits on assets. To address this, we introduce a lightweight per-instance texture refinement procedure. This procedure fine-tunes the triplane representation and the NeRF color estimation model on the mesh surface using the input multi-view images in just 4 seconds. This refinement improves the PSNR to 29.79 and achieves faithful reconstruction of complex textures, such as text. Additionally, our approach enables various downstream applications, including text- or image-to-3D generation.<|reference_end|>
@article{zhuang2024gtr:,
    title={GTR: Improving Large 3D Reconstruction Models through Geometry and
  Texture Refinement},
    author={Peiye Zhuang, Songfang Han, Chaoyang Wang, Aliaksandr Siarohin, Jiaxu
  Zou, Michael Vasilkovsky, Vladislav Shakhrai, Sergey Korolev, Sergey
  Tulyakov, Hsin-Ying Lee},
    journal={arXiv preprint arXiv:2406.05649},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05649},
    primaryClass={cs.CV cs.AI}
}