Estimating accurate depth from a single image is challenging because it is an
ill-posed problem as infinitely many 3D scenes can be projected to the same 2D
scene. However, recent works based on deep convolutional neural networks show
great progress with plausible results. The convolutional neural networks are
generally composed of two parts: an encoder for dense feature extraction and a
decoder for predicting the desired depth. In the encoder-decoder schemes,
repeated strided convolution and spatial pooling layers lower the spatial
resolution of transitional outputs, and several techniques such as skip
connections or multi-layer deconvolutional networks are adopted to recover the
original resolution for effective dense prediction. In this paper, for more
effective guidance of densely encoded features to the desired depth prediction,
we propose a network architecture that utilizes novel local planar guidance
layers located at multiple stages in the decoding phase. We show that the
proposed method outperforms the state-of-the-art works with significant margin
evaluating on challenging benchmarks. We also provide results from an ablation
study to validate the effectiveness of the proposed method.

本文介绍了一种网络架构，利用位于解码阶段多个阶段的新型本地平面指导层来更有效地引导密集编码特征以进行所需的深度预测， 并在具有挑战性的基准测试中通过消融研究证明所提出的方法优于最先进的作品。