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(DE)^2 CO: Deep Depth Colorization

Abstract · Mar 31, 2017 12:30 ·


Arxiv Abstract

  • F. M. Carlucci
  • P. Russo
  • S. M. Baharlou
  • B. Caputo

Object recognition on depth images using convolutional neural networks requires mapping the data collected with depth sensors into three dimensional channels. This makes them processable by deep architectures, pre-trained over large scale RGB databases like ImageNet. Current mappings are based on heuristic assumptions over what depth properties should be most preserved, resulting often in cumbersome data visualizations, and likely in sub-optimal recognition results. Here we take an alternative route and we attempt instead to \emph{learn} an optimal colorization mapping for any given pre-trained architecture, using as training data a reference RGB-D database. We propose a deep network architecture, exploiting the residual paradigm, that learns how to map depth data to three channel images from a reference database. A qualitative analysis of the images obtained with this approach clearly indicates that learning the optimal mapping for depth data preserves the richness of depth information much better than hand-crafted approaches currently in use. Experiments on the Washington, JHUIT-50 and BigBIRD public benchmark databases, using AlexNet, VGG-16, GoogleNet, ResNet and SqueezeNet, clearly showcase the power of our approach, with gains in performance of up to $17\%$ compared to the state of the art.

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