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GTC On-Demand

AI and DL Research
Presentation
Media
Tackling 3D ToF Artifacts through Learning and the FLAT Dataset
Abstract:
We will discuss a deep learning-based method for improving the quality of 3D reconstruction performed by time-of-flight cameras. Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by these sensors. We'll explain our proposed two-stage, deep-learning approach to address all of these sources of artifacts simultaneously. We'll also introduce FLAT, a synthetic dataset of 2000 ToF measurements that capture all of these nonidealities and can be used to simulate different hardware. Using the Kinect camera as a baseline, we show improved reconstruction errors on simulated and real data, as compared with state-of-the-art methods.
 
Topics:
AI and DL Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9318
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