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Object recognition using 3D convolutional neural networks
Moravec, Jaroslav ; Lokoč, Jakub (advisor) ; Straka, Milan (referee)
Title: Object recognition using 3D convolutional neural networks Author: Jaroslav Moravec Department: Department of Software Engineering Supervisor: RNDr. Jakub Lokoč, Ph.D., Department of Software Engineering Abstract: With the fast development of laser and sensor technologies, it has become easy to scan a real-world object and save it in a digital format into a persistent database. With the rising number of scanned 3D objects, data man- agement and retrieval methods become necessary. For various retrieval tasks, effective retrieval models are required. In our work, we focus on effective classifi- cation and similarity search. The investigated approach is based on convolutional neural networks representing a machine learning method that boomed in recent years. We have designed and trained several architectures of 3D convolutional neural networks and tested them on state-of-the-art benchmark 3D datasets for 3D object recognition and retrieval. We were also able to show that the trained features on one dataset can be then used to predict class labels on another 3D dataset. Keywords: Object recognition, 3D convolution, neural networks
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