Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset

Qi Guo, Iuri Frosio, Orazio Gallo, Todd Zickler, Jan Kautz

ECCV 2018

doi:10.1007/978-3-030-01246-5_23 /eccv/2018/guo2018eccv-tackling/

Abstract

Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by time-of-flight cameras. We propose a two-stage, deep-learning approach to address all of these sources of artifacts simultaneously. We also introduce FLAT, a synthetic dataset of 2000 ToF measurements that capture all of these nonidealities, and allows to simulate different camera hardware. Using the Kinect 2 camera as a baseline, we show improved reconstruction errors over state-of-the-art methods, on both simulated and real data.

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Cite

Text

Guo et al. "Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01246-5_23

Markdown

[Guo et al. "Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/guo2018eccv-tackling/) doi:10.1007/978-3-030-01246-5_23

BibTeX

@inproceedings{guo2018eccv-tackling,
  title     = {{Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset}},
  author    = {Guo, Qi and Frosio, Iuri and Gallo, Orazio and Zickler, Todd and Kautz, Jan},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2018},
  doi       = {10.1007/978-3-030-01246-5_23},
  url       = {https://mlanthology.org/eccv/2018/guo2018eccv-tackling/}
}