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GTC ON-DEMAND

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Abstract:
Voxelized representation of 3D objects is commonly used for training 3D-Convolutional Neural Networks for object detection and classification. However, high-resolution voxelization of CAD models are memory intensive and hence, it is not possible to load multiple models in the GPU for training. We have developed a GPU-accelerated voxelization technique that generates multi-level voxel grids of 3D objects. Instead of creating a single high-resolution voxel grid for the whole object, this technique generates selective region-based high-resolution voxel grids to represent detailed features in the object. We have also developed a multi-resolution 3D-Convolutional Neural Network that uses this hybrid voxelization for accurate object recognition and classification.
Voxelized representation of 3D objects is commonly used for training 3D-Convolutional Neural Networks for object detection and classification. However, high-resolution voxelization of CAD models are memory intensive and hence, it is not possible to load multiple models in the GPU for training. We have developed a GPU-accelerated voxelization technique that generates multi-level voxel grids of 3D objects. Instead of creating a single high-resolution voxel grid for the whole object, this technique generates selective region-based high-resolution voxel grids to represent detailed features in the object. We have also developed a multi-resolution 3D-Convolutional Neural Network that uses this hybrid voxelization for accurate object recognition and classification.  Back
 
Topics:
AI & Deep Learning Research, Industrial Inspection, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8389
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