Method, device, terminal and storage medium for depth estimation of monocular video

Abstract

The embodiment of the invention discloses a monocular video depth estimation method, device, terminal and storage medium. The method includes: obtaining a sequence of image frames of a monocular video, and calculating the pose relationship between two adjacent image frames in the sequence according to a camera pose estimation algorithm; sequentially using each image frame in the sequence as a preset neural network model Input, and determine the initial depth map and initial uncertainty distribution map of each image frame according to the output of the preset neural network model; perform inter-frame information according to each attitude relationship and the initial depth map and initial uncertainty distribution map of each image frame Transfer and fusion, determine the final depth map and the final uncertainty distribution map of each image frame in turn. The technical solution of the embodiment of the present invention can perform depth restoration on the image frame of the monocular video, which not only improves the prediction accuracy of the depth map, but also can obtain the uncertainty distribution of the depth map.

Description

technical field [0001] Embodiments of the present invention relate to image processing technologies, and in particular to a method, device, terminal and storage medium for depth estimation of monocular video. Background technique [0002] In the field of computer vision research, more and more people are studying the monocular depth estimation method, that is, using the mapping relationship between the hidden visual information in a single image, such as size, shadow, plane, etc., and the real depth value. Do depth estimation. Monocular depth estimation has many applications, such as scene understanding, semantic segmentation, 3D modeling, robot obstacle avoidance, etc. Traditional monocular estimation methods mainly rely on Structure-from-Motion (SfM) technology, or Simultaneous Localization and Mapping (SLAM) technology based on monocular cameras, which is widely used in the field of robotics. SfM and SLAM use multi-view images to estimate the attitude of the monocular c...

AI Technical Summary

Problems solved by technology

[0004] However, the existing supervised learning CNN model for monocular depth estimation can only predict and output the depth map, and cannot estimate and output the uncertainty distribution map corresponding to the depth map at the same time, making the existing monocular depth estimation The accuracy of the depth map on the network model preview side is not high

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