CNN based methods: Literature review Depth completion and Sparse data

Abstract

In last decade, With the rise of neural networks Depth completion has become extensive attention recently due to the development of autonomous driving, which aims to recover dense depth points from sparse depth measurements. In this thesis first introduces the rise and development of deep learning and convolution neural network and summarizes the basic traditional methods, and a brief background of depth estimation, pooling operation of convolution neural network convolution feature extraction. Convolutional spatial propagation network (CSPN) is one of the state-of-the-art methods and we are going to dig through all the methods to do a performance comparison with the traditional methods. Then, the research status and development trend of convolution neural network based on depth completion in sparse data are reviewed, which is mainly introduced from the aspects of typical network structure construction, framework, training method and performance. Finally, some problems in the current research are briefly summarized and discussed. Later, we will conclude the literature review based on pros and cons od state-ofthe-art methods and how recent research has move forward for CNN with depth completion on sparse data for current computer vision problems.