Supervised data driving-based monocular video depth estimating method

Abstract

The invention discloses a supervised data driving-based monocular video depth estimating method comprising the following steps: (1) a sample video sequence and a corresponding depth sequence are obtained as a training data set; (2) a tracking-based superpixel segmentation method is used for segmenting the training data set, and characteristics of each segmented unit are extracted; (3) a network model combining a convolution nerve network and a space-time condition random field; (4) the training data set, the segmentation result and the corresponding characteristics are used for training a depth space-time convolution nerve network field model; (5) a video sequence to be estimated is segmented, and characteristics of each segmented unit are extracted; (6) the video sequence to be estimated, the segmentation result and the corresponding characteristics are input into the trained model, and a depth sequence can be obtained. The supervised data driving-based monocular video depth estimating method is based on consideration on space-time consistency and hierarchical relation accuracy, and monocular stereoscopic video quality can be improved.

Description

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AI Technical Summary

Benefits of technology

This new technology allows us to estimate 360 degrees from 2D images without relying solely upon ground truth values like distance or camera angle measurements. It uses both spatial and temporal variables together to make accurate estimations even when there may have errors due to uneven surfaces such as rocks or water waves that could affect how well an image looks at it.

Problems solved by technology

This patented describes two different ways to improve depth estimate performance when making sense of three dimensional videos. One way involves analyzing existing techniques such as frame rate conversion structures and depth estimation algorithms like those described earlier. Another solution proposes utilizing advanced technologies like Deep Learning to enhance depth estimates without relying heavily on predefined models.

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