1st Place Solution for ICCV 2023 OmniObject3D Challenge: Sparse-View Reconstruction
<|reference_start|>1st Place Solution for ICCV 2023 OmniObject3D Challenge: Sparse-View Reconstruction: In this report, we present the 1st place solution for ICCV 2023 OmniObject3D Challenge: Sparse-View Reconstruction. The challenge aims to evaluate approaches for novel view synthesis and surface reconstruction using only a few posed images of each object. We utilize Pixel-NeRF as the basic model, and apply depth supervision as well as coarse-to-fine positional encoding. The experiments demonstrate the effectiveness of our approach in improving sparse-view reconstruction quality. We ranked first in the final test with a PSNR of 25.44614.<|reference_end|>
@article{du20241st,
    title={1st Place Solution for ICCV 2023 OmniObject3D Challenge: Sparse-View
  Reconstruction},
    author={Hang Du, Yaping Xue, Weidong Dai, Xuejun Yan, Jingjing Wang},
    journal={arXiv preprint arXiv:2404.10441},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10441},
    primaryClass={cs.CV}
}