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Surface Normals in the Wild

Abstract · Apr 10, 2017 17:13 ·

depth surface wild annotations relative normal normals chen metric estimation cs-cv

Arxiv Abstract

  • Weifeng Chen
  • Donglai Xiang
  • Jia Deng

We study the problem of single-image depth estimation for images in the wild. We collect human annotated surface normals and use them to train a neural network that directly predicts pixel-wise depth. We propose two novel loss functions for training with surface normal annotations. Experiments on NYU Depth and our own dataset demonstrate that our approach can significantly improve the quality of depth estimation in the wild.

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