V. Miclea, S. Nedevschi
Proceedings of 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, 2018, pp. 300-306.
We propose a new real-time depth upsampling method based on convolutional neural networks (CNNs) that uses the local context provided by semantic information. Two solutions based on convolutional networks are introduced, modeled according to the level of sparsity given by the depth sensor. While first CNN upsamples data from a partial-dense input, the second one uses dilated convolutions as means to cope with sparse inputs from cost-effective depth sensors. Experiments over data extracted from Kitti dataset highlight the performance of our methods while running in real-time (11 ms for the first case and 17 ms for the second) on a regular GPU.