Changed to 2D data and specified SSD as detection network
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General Idea of the Thesis:
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1. Realistic scene with objects of YCB Video data set (self recorded or from data set).
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2. Retrieve point cloud from scene.
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2. Retrieve 2D data from scene.
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3. Segment point cloud into separate areas for objects.
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4. a) Feed neural network with 3D data for object. Classify object and calculate classification loss (cross entropy loss).
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b) Feed autoencoder with 3D data for object. Calculate encoding loss.
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5. In testing phase the encoding loss tells us if an object is unknown.
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4. a) Feed SSD with 2D data for object. Classify object and calculate classification loss (cross entropy loss).
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b) Feed autoencoder with 2D data for object. Calculate encoding/decoding loss.
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5. In testing phase the encoding/decoding loss tells us if an object is unknown.
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