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METHOD FOR ESTIMATING HIGH-QUALITY DEPTH MAPS BASED ON DEPTH PREDICTION AND ENHANCEMENT SUBNETWORKS

机译:基于深度预测和增强子网络的高质量深度图估计方法

摘要

The present invention provides a method for estimating high-quality depth map based on depth prediction and enhancement sub-networks, belonging to the technical field of image processing and computer vision. This method constructs depth prediction subnetwork to predict depth information from color image and uses depth enhancement subnetwork to obtain high-quality depth map by recovering the low-resolution depth map. It is easy to construct the system, and can obtain the high-quality depth map from the corresponding color image directly by the well-trained end to end network. The algorithm is easy to be implemented. It uses high-frequency component of color image to help to recover the lost depth boundaries information caused by down-sampling operators in depth prediction sub-network, and finally obtains high-quality and high-resolution depth maps. It uses spatial pyramid pooling structure to increase the accuracy of depth map prediction for multi-scale objects in the scene.
机译:本发明提供了一种基于深度预测和增强子网络的高质量深度图估计方法,属于图像处理和计算机视觉技术领域。该方法构造深度预测子网以从彩色图像中预测深度信息,并使用深度增强子网通过恢复低分辨率深度图获得高质量的深度图。该系统易于构建,并且可以通过训练有素的端到端网络直接从相应的彩色图像中获得高质量的深度图。该算法易于实现。它利用彩色图像的高频成分来帮助恢复深度预测子网中由于下采样算子而导致的深度边界丢失信息,最终获得高质量,高分辨率的深度图。它使用空间金字塔池结构来提高场景中多尺度对象的深度图预测的准确性。

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