Depth from time-of-flight using machine learning
First Claim
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1. A depth detection apparatus comprising:
- a memory storing raw time-of-flight sensor data received from a time-of-flight sensor; and
a processor comprising a trained machine learning component having been trained using training data pairs, a training data pair comprising at least one simulated raw time-of-flight sensor frame and a corresponding simulated ground truth depth map;
the trained machine learning component configured to compute in a single stage, for an item of the stored raw time-of-flight sensor data, a depth map of a surface depicted by the item, by pushing the item through the trained machine learning component.
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Abstract
A depth detection apparatus is described which has a memory storing raw time-of-flight sensor data received from a time-of-flight sensor. The depth detection apparatus also has a trained machine learning component having been trained using training data pairs. A training data pair comprises at least one simulated raw time-of-flight sensor data value and a corresponding simulated ground truth depth value. The trained machine learning component is configured to compute in a single stage, for an item of the stored raw time-of-flight sensor data, a depth value of a surface depicted by the item, by pushing the item through the trained machine learning component.
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Citations
20 Claims
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1. A depth detection apparatus comprising:
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a memory storing raw time-of-flight sensor data received from a time-of-flight sensor; and a processor comprising a trained machine learning component having been trained using training data pairs, a training data pair comprising at least one simulated raw time-of-flight sensor frame and a corresponding simulated ground truth depth map; the trained machine learning component configured to compute in a single stage, for an item of the stored raw time-of-flight sensor data, a depth map of a surface depicted by the item, by pushing the item through the trained machine learning component. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A depth detection apparatus comprising:
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a memory storing frames of raw time-of-flight sensor data received from a time-of-flight sensor; and a trained machine learning component having been trained using training data pairs, a training data pair comprising a simulated raw time-of-flight sensor frame and a corresponding simulated ground truth depth map; the trained machine learning component configured to compute in a single stage, for a frame of the stored raw time-of-flight sensor data, a depth map of surfaces depicted by the frame, by pushing the frame through the trained machine learning component. - View Dependent Claims (17, 18, 19)
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20. A computer-implemented method comprising:
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storing, at a memory, raw time-of-flight sensor data received from a time-of-flight sensor; and operating, by a processor, a trained machine learning component having been trained using training data pairs, a training data pair comprising at least one simulated raw time-of-flight sensor frame and a corresponding simulated ground truth depth value; wherein operating the trained machine learning component comprises computing, in a single stage, for an item of the stored raw time-of-flight sensor data, a depth map of a surface depicted by the item, by pushing the item through the trained machine learning component.
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Specification