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》 资源 》论文》Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset

Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset

2019-10-22 | |  45 |   32 |   0
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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