High Quality Monocular Depth Estimation via Transfer Learning

31 Dec 2018  ·  Ibraheem Alhashim, Peter Wonka ·

Accurate depth estimation from images is a fundamental task in many applications including scene understanding and reconstruction. Existing solutions for depth estimation often produce blurry approximations of low resolution. This paper presents a convolutional neural network for computing a high-resolution depth map given a single RGB image with the help of transfer learning. Following a standard encoder-decoder architecture, we leverage features extracted using high performing pre-trained networks when initializing our encoder along with augmentation and training strategies that lead to more accurate results. We show how, even for a very simple decoder, our method is able to achieve detailed high-resolution depth maps. Our network, with fewer parameters and training iterations, outperforms state-of-the-art on two datasets and also produces qualitatively better results that capture object boundaries more faithfully. Code and corresponding pre-trained weights are made publicly available.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Monocular Depth Estimation KITTI Eigen split DenseDepth absolute relative error 0.093 # 17
Monocular Depth Estimation NYU-Depth V2 DenseDepth RMSE 0.465 # 18

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