Monocular image depth estimation using dilated convolution and spatial pyramid polling structure

Yinzhang Ding; Lu Lin; Lianghao Wang; Ming Zhang; Dongxiao Li; Haojie Ma

doi:10.1117/12.2524357

6 May 2019 Monocular image depth estimation using dilated convolution and spatial pyramid polling structure

Yinzhang Ding, Lu Lin, Lianghao Wang, Ming Zhang, Dongxiao Li, Haojie Ma

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 1106918 (2019) https://doi.org/10.1117/12.2524357
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

In this work, we address the problem of depth estimation from a single image. This is a challenging task because a single still image on its own does not give much depth cue, while recent advances in CNNs have made learning and predicting depth from a single image possible. We propose a new residual convolutional neural network (CNN) with dilated convolution and spatial pyramid pooling (SPP) structure to model the ambiguous mapping from a monocular 2D image to its depth map. The advantages of our method come from the use of dilated convolution and multi spatial scale information. Compared with existing deep CNN based methods, our method achieves much better results in indoor and outdoor scenarios.

Citation Download Citation

Yinzhang Ding, Lu Lin, Lianghao Wang, Ming Zhang, Dongxiao Li, and Haojie Ma "Monocular image depth estimation using dilated convolution and spatial pyramid polling structure", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 1106918 (6 May 2019); https://doi.org/10.1117/12.2524357

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