Mr. Yinzhang Ding Profile

Yinzhang Ding

at Zhejiang Univ

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Proceedings Article | 27 January 2021 Paper

Semantic reconstruction based on RGB image and sparse depth

Yu Cai, Yinzhang Ding, Dongxiao Li, Ming Zhang

Proceedings Volume 11720, 1172025 (2021) https://doi.org/10.1117/12.2589405

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With the increasing popularity of applications such as unmanned driving, the ability of environment perception has become more and more important, and the most common expression of environment perception is semantic reconstruction. Therefore, more and more researchers are trying to synthesize the information from multiple sensors to achieve better semantic reconstruction effects. However, most of the current estimation methods (a). Too bulky to run in real-time (b). Failure to effectively use the information of a variety of different sensors (c). Failure to generate sufficient environmental perception information under limited computing power, such as semantic information and depth information. Therefore, this paper proposes a multi-modal joint estimation network for semantic reconstruction, which can solve the above problems. Our method takes RGB image and sparse depth as input. By adding multi-scale information to the neural network, it outputs semantic segmentation and depth recovery results simultaneously while maintaining light-weighted and real-time performance, then fuses both results in point clouds to get better environment perception ability. A large number of experiments show that our method has better performance than other methods in the same application scenario.

Proceedings Article | 6 May 2019 Paper

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, 1106918 (2019) https://doi.org/10.1117/12.2524357

KEYWORDS: Convolution, RGB color model, Data modeling, Image analysis, Network architectures, Feature extraction, 3D modeling

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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.

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