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.
CITATION STYLE
Guo, Q., Frosio, I., Gallo, O., Zickler, T., & Kautz, J. (2018). Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11205 LNCS, pp. 381–396). Springer Verlag. https://doi.org/10.1007/978-3-030-01246-5_23
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