1st Place Solution for ICCV 2023 OmniObject3D Challenge: Sparse-View Reconstruction

Hang Du, Yaping Xue, Weidong Dai, Xuejun Yan, Jingjing Wang
4/16/2024
cs.CV

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Abstract

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.

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Cite this paper

@article{du2024place,
  title  = {1st Place Solution for ICCV 2023 OmniObject3D Challenge: Sparse-View Reconstruction},
  author = {Hang Du and Yaping Xue and Weidong Dai and Xuejun Yan and Jingjing Wang},
  year   = {2024},
  eprint = {2404.10441},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.10441v1}
}

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