Poxel: Voxel Reconstruction for 3D Printing

Ruixiang Cao, Satoshi Yagi, Satoshi Yamamori, Jun Morimoto
1/16/2025
cs.GRcs.CV

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Abstract

Recent advancements in 3D reconstruction, especially through neural rendering approaches like Neural Radiance Fields (NeRF) and Plenoxel, have led to high-quality 3D visualizations. However, these methods are optimized for digital environments and employ view-dependent color models (RGB) and 2D splatting techniques, which do not translate well to physical 3D printing. This paper introduces "Poxel", which stands for Printable-Voxel, a voxel-based 3D reconstruction framework optimized for photopolymer jetting 3D printing, which allows for high-resolution, full-color 3D models using a CMYKWCl color model. Our framework directly outputs printable voxel grids by removing view-dependency and converting the digital RGB color space to a physical CMYKWCl color space suitable for multi-material jetting. The proposed system achieves better fidelity and quality in printed models, aligning with the requirements of physical 3D objects.

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Cite this paper

@article{cao2025poxel,
  title  = {Poxel: Voxel Reconstruction for 3D Printing},
  author = {Ruixiang Cao and Satoshi Yagi and Satoshi Yamamori and Jun Morimoto},
  year   = {2025},
  eprint = {2501.10474},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.10474v1}
}

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