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arXiv · 1807.10376

Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset

Qi Guo·Iuri Frosio·Orazio Gallo·Todd Zickler·Jan Kautz
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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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Qi Guo, Iuri Frosio, Orazio Gallo, Todd Zickler, Jan Kautz. 2018-07-26. Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset. https://arxiv.org/abs/1807.10376

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