Sparse 3D convolutional neural networks
This work addresses the need for efficient 3D data processing in applications like object recognition, but it appears incremental as it builds on existing CNN methods.
The authors tackled the problem of processing sparse 3D data by implementing a convolutional neural network, experimenting with 2D triangular-lattice and 3D tetrahedral-lattice CNNs for efficiency, but no concrete results or numbers are reported.
We have implemented a convolutional neural network designed for processing sparse three-dimensional input data. The world we live in is three dimensional so there are a large number of potential applications including 3D object recognition and analysis of space-time objects. In the quest for efficiency, we experiment with CNNs on the 2D triangular-lattice and 3D tetrahedral-lattice.