Single-frame image three-dimensional reconstruction device and method based on deep learning

Abstract

The invention discloses a single-frame image three-dimensional reconstruction device and method based on deep learning. The device comprises an upper computer, a support frame and a high-definition camera at the top thereof. Three parallel light LED area light sources in 120 degrees are installed in equal height around the high-definition camera. The method comprises the following steps: shootinga training sample, using images simultaneously irradiated by red green blue lights as input data needed by a training model, taking a gray value of three images successively irradiated by the white light, turning into the single-channel image as a truth-value for the training model, using a deep learning method and the input data, constructing a full linked network model of per-pixels for single-frame image three-dimensional reconstruction, training the model, continuously regulating and optimizing network parameters by using a counterpropagation algorithm, forecasting target surface three-dimensional information, and three-dimensionally re-constructing the result of the network forecast by using a luminosity three-dimensional algorithm, to obtain the object surface three-dimensional information. The method is capable of, through improving the network structure, constructing the network model for the single-frame image three-dimensional reconstruction, and increasing the application scene compared with the multi-frame image three-dimensional reconstruction.

Description

technical field [0001] The invention relates to a device for three-dimensional reconstruction of a single-frame image, and the key is a method for three-dimensional reconstruction of a single-frame image based on deep learning, which belongs to the field of three-dimensional reconstruction of a single-frame image Background technique [0002] 3D reconstruction refers to the process of establishing a mathematical model of a 3D object in a real scene in a computer, and it is a popular research direction in the field of computer vision. Compared with two-dimensional images, three-dimensional models can provide depth data of objects, so that they can more comprehensively display the characteristics of objects, so they are widely used in many fields such as computer animation, human-computer interaction, and modern medicine. [0003] However, most of the 3D reconstruction algorithms are 3D reconstruction of multi-frame images. In practical applications, multi-frame images have gr...

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Problems solved by technology

[0003] However, most of the 3D reconstruction algorithms are 3D reconstruction of multi-frame images. In practical applications, multi-frame images have great limitations and cannot be reconstructed in real time, and cannot be used for 3D reconstruction of dynamic non-rigid objects.

However, for the traditional single-frame reconstruction method, the accuracy is not high, and the surface details cannot be reconstructed, so it is difficult to apply to scenes requiring high precision

The previous 3D reconstruction algorithm based on deep learning cannot achieve single frame, poor accuracy, poor color robustness, and cannot three-dimensionally reconstruct objects with multiple colors on the surface

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