T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Image
<|reference_start|>T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Image: Pixel2Mesh (P2M) is a classical approach for reconstructing 3D shapes from a single color image through coarse-to-fine mesh deformation. Although P2M is capable of generating plausible global shapes, its Graph Convolution Network (GCN) often produces overly smooth results, causing the loss of fine-grained geometry details. Moreover, P2M generates non-credible features for occluded regions and struggles with the domain gap from synthetic data to real-world images, which is a common challenge for single-view 3D reconstruction methods. To address these challenges, we propose a novel Transformer-boosted architecture, named T-Pixel2Mesh, inspired by the coarse-to-fine approach of P2M. Specifically, we use a global Transformer to control the holistic shape and a local Transformer to progressively refine the local geometry details with graph-based point upsampling. To enhance real-world reconstruction, we present the simple yet effective Linear Scale Search (LSS), which serves as prompt tuning during the input preprocessing. Our experiments on ShapeNet demonstrate state-of-the-art performance, while results on real-world data show the generalization capability.<|reference_end|>
@article{zhang2024t-pixel2mesh:,
    title={T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh
  Generation from a Single Image},
    author={Shijie Zhang, Boyan Jiang, Keke He, Junwei Zhu, Ying Tai, Chengjie
  Wang, Yinda Zhang, Yanwei Fu},
    journal={arXiv preprint arXiv:2403.13663},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13663},
    primaryClass={cs.CV}
}