Training method and device of optical flow estimation model
A technology for estimating models and training methods, applied in computing, computer components, character and pattern recognition, etc., can solve the problems of low generalization, difficult to obtain optical flow labels, and low accuracy, and achieve the effect of reducing the impact
Pending Publication Date: 2022-03-25
BEIJING HORIZON INFORMATION TECH CO LTD
View PDF0 Cites 0 Cited by
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
The supervised optical flow estimation method based on deep learning usually requires a large number of labels for model training, but the optical flow labels of real scenes are very difficult to obtain, and the model based on virtual data training often has the problem of low generalization in real scenes.
[0003] In related technologies, in the absence of optical flow labels, the self-supervised method is generally used for model training. During the training process, based on the assumption of photometric consistency between frames, the photometric error loss function is used to train the optical flow estimation model. However, this self-supervised The model training method of the model is less accurate
Method used
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View moreImage
Smart Image Click on the blue labels to locate them in the text.
Smart ImageViewing Examples
Examples
Experimental program
Comparison scheme
Effect test
Embodiment Construction
[0035] Hereinafter, exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. Apparently, the described embodiments are only some of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure, and it should be understood that the present disclosure is not limited by the exemplary embodiments described here.
[0036] It should be noted that relative arrangements of components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
[0037] Those skilled in the art can understand that terms such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, etc. necessary logical sequence.
[0038] It should also be understood that in the embodiments of the present disclosure, "...
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More PUM
Login to View More Abstract
The embodiment of the invention discloses an optical flow estimation model training method and device, and the method comprises the steps: carrying out the semantic segmentation of a first sample image and a second sample image, and obtaining a first semantic segmentation result and a second semantic segmentation result; determining a first static region in the first sample image and a second static region in the second sample image based on the first semantic segmentation result and the second semantic segmentation result; determining a pixel point mapping relation between the first sample image and the second sample image based on the inter-frame attitude information of the first sample image and the second sample image and the point cloud data of the first sample image; determining a first optical flow between the first static region and the second static region based on a pixel point mapping relationship; based on the first optical flow, training of the first optical flow estimation model is constrained. The precision of the optical flow estimation model trained by the embodiment of the invention is obviously higher than that of a self-supervision method, and the shadow problem can be solved.
Description
technical field [0001] The present disclosure relates to the technical field of image processing and artificial intelligence (AI), in particular to a training method and device for an optical flow estimation model. Background technique [0002] Dense optical flow estimation is to calculate the offset of all points on the image to form a dense optical flow field, and then based on the dense optical flow field, pixel-level image registration can be performed. Dense optical flow estimation has a wide range of applications in the fields of autonomous driving and autonomous robots. In recent years, with the development of deep learning technology, dense optical flow estimation technology based on deep learning has achieved good results. The supervised optical flow estimation method based on deep learning usually requires a large number of labels for model training, but the optical flow labels of real scenes are very difficult to obtain, and the model based on virtual data traini...
Claims
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More Application Information
Patent Timeline
Login to View More IPC IPC(8): G06K9/62G06T7/11G06V10/774
CPCG06T7/11G06T2207/20132G06F18/214
Inventor 于雷隋伟张骞黄畅
Owner BEIJING HORIZON INFORMATION TECH CO LTD