OctNetFusion: Learning Depth Fusion from Data

@article{Riegler2017OctNetFusionLD,
  title={OctNetFusion: Learning Depth Fusion from Data},
  author={Gernot Riegler and Ali O. Ulusoy and Horst Bischof and Andreas Geiger},
  journal={2017 International Conference on 3D Vision (3DV)},
  year={2017},
  pages={57-66}
}
In this paper, we present a learning based approach to depth fusion, i.e., dense 3D reconstruction from multiple depth images. The most common approach to depth fusion is based on averaging truncated signed distance functions, which was originally proposed by Curless and Levoy in 1996. While this method is simple and provides great results, it is not able to reconstruct (partially) occluded surfaces and requires a large number frames to filter out sensor noise and outliers. Motivated by the… CONTINUE READING
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