Depth map estimation method and system

A depth map and depth technology, applied in neural learning methods, calculations, image analysis, etc., can solve the problems of depth map estimation accuracy and reliability, and achieve the effect of improving accuracy

Active Publication Date: 2022-07-29
杭州图科智能信息科技有限公司
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Problems solved by technology

[0004] In view of this, an embodiment of the present invention provides a method and system for estimating a depth map, which is used to solve the problem of doubtful accuracy and reliability of the existing depth map estimation

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  • Depth map estimation method and system

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[0028] In order to make the purpose, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the following The described embodiments are only some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0029] It should be understood that the term "comprising" and other similar meaning expressions in the description or claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusion, such as a process, method or system, and equipment comprising a series of steps or units. Not limited to the ...

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Abstract

The invention provides a depth map estimation method and system. The method comprises the following steps: carrying out multi-scale depth feature extraction on an image; uniformly sampling in a scene depth range to obtain depth hypotheses under different scales; under the depth hypothesis of each scale, the depth features of the neighborhood view are transformed to a reference view through microhomography transformation, and a cost body is constructed through group correlation measurement; regularizing the cost body based on a three-dimensional convolutional neural network, obtaining a depth probability body and an uncertainty probability body through a logistic regression algorithm, and respectively estimating a corresponding depth map and an uncertainty map; performing up-sampling and normalization on the uncertainty map and the depth map to obtain a sampling interval and a depth hypothesis of a next scale; and sampling depth hypotheses of different scales, and training and supervising the depth map and the uncertainty map under each scale through an uncertain sensing loss function. According to the scheme, the accuracy and reliability of image depth estimation can be effectively improved.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to a depth map estimation method and system. Background technique [0002] Multi-view depth estimation aims to build dense correspondences from multiple images with known camera poses, thereby recovering the dense geometry from the reference viewpoint. In recent years, deep learning techniques have greatly promoted the development of multi-view depth estimation. Since the deep learning-based multi-view depth estimation method needs to use the 3D convolutional neural network to regularize the 3D cost volume, this makes the network memory consumption and computation time increase cubically as the input image resolution increases. [0003] In order to be able to estimate high-resolution depth maps and improve computational speed, the "coarse-to-fine" strategy is widely used in deep learning-based multi-view depth estimation. Such methods usually first perform depth sampling...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/55G06N3/04G06N3/08
CPCG06T7/55G06N3/08G06T2207/10028G06T2207/20081G06T2207/20084G06N3/045
Inventor 陶文兵苏婉娟刘李漫
Owner 杭州图科智能信息科技有限公司
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