Single-image three-dimensional reconstruction method based on deep learning video supervision

Abstract

The invention provides a single-image three-dimensional reconstruction method based on deep learning video supervision. The single-image three-dimensional reconstruction method comprises the followingsteps: 1) constructing an object pose prediction module, wherein the position of camera shooting relative to an object can be obtained according to the object in an input image; 2) constructing an object three-dimensional shape estimation module: obtaining a three-dimensional point cloud of an input single object image through iterative optimization loss prediction; 3) constructing a multi-frameshape fusion module: inputting a video graph line sequence into the above two modules in parallel to obtain single-frame camera pose and three-dimensional shape prediction, and obtaining more accurateprediction through multi-frame weight fusion, consistency constraint and smoothness constraint; and 4) arranging an overall training framework which comprises three stages of data preprocessing, model framework training and testing. End-to-end three-dimensional reconstruction is realized, training can be performed by using a video sequence, and the three-dimensional point cloud can be predicted only by using a single image in a test stage.

Description

[0001] technology neighborhood [0002] The invention belongs to the technical field of three-dimensional reconstruction, and in particular relates to a three-dimensional single-image reconstruction method based on deep learning video supervision. Background technique [0003] In recent years, with the development of deep learning, the solution to computer vision problems has been developed to a greater extent. Recently, various 2D image processing techniques have been gradually perfected and applied to 3D problems, and the reconstruction of the 3D shape of objects has also become one of the hot issues. Many previous methods require complete 3D model data for supervision, but such data is scarce and the acquisition process is complex and expensive. As a result, many multi-image and single-image reconstruction methods have emerged. Due to the weakening of supervisory information, it will lead to the reduction of local detail accuracy and the ambiguity of object perspective. A...

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Problems solved by technology

Although these reconstruction methods can obtain ideal 3D reconstruction results, due to the difficulty and cost of obtaining 3D data, the input constraints of multi-image correlation, the time-consuming and memory-consuming operation process, and the sensitivity to input order, etc., All the above methods have disadvantages

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