Check patentability & draft patents in minutes with Patsnap Eureka AI!

A Depth Estimation Method for Outdoor Monocular Image Based on Structured Random Forest

A random forest and image depth technology, applied in the field of depth estimation, can solve the problem of low depth accuracy and achieve the effect of improving accuracy

Active Publication Date: 2021-07-27
HUAZHONG UNIV OF SCI & TECH
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Aiming at the defects of the prior art, the purpose of the present invention is to solve the technical problem that the depth accuracy predicted by the prior art is not high

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 more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A Depth Estimation Method for Outdoor Monocular Image Based on Structured Random Forest
  • A Depth Estimation Method for Outdoor Monocular Image Based on Structured Random Forest
  • A Depth Estimation Method for Outdoor Monocular Image Based on Structured Random Forest

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0043] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0044] Different from indoor images, outdoor images have unique location features such as sky and ground, and there is also a strong correlation in the vertical direction of the image. Using this information can better learn the structure of the scene to estimate the depth. Most of the machine learning methods only consider the selection of features, but ignore the structural information of the scene. This invention proposes a method for estimating the depth of outdoor monocular images based on structured random forests: first make an assumption that the features are similar The corresponding depth...

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

No PUM Login to View More

Abstract

The invention discloses a method for estimating the depth of an outdoor monocular image based on a structured random forest, belonging to the field of depth estimation. The present invention uses the features of image blocks to classify, endows image blocks with similar features with the same depth, uses structured random forest to predict image depth, and through information gain, each node discretizes the depth structure label of the scene, and then Classify until the similarity of image blocks in each leaf node in the tree reaches a certain threshold. Finally, the results of each local block are combined to form a complete depth map prediction result. Multi-faceted features can obtain reliable depth clues, block can better consider the structure and local information of the scene, and use the existing depth information to estimate accurate and reliable absolute depth. By discretizing the depth structure labels at multiple nodes, classifying the structure of the depth block is beneficial to the estimation of the depth, and the accuracy of the estimated depth can be better improved by randomly combining the results of multiple trees.

Description

technical field [0001] The invention belongs to the field of depth estimation, and more specifically relates to a method for estimating the depth of an outdoor monocular image based on a structured random forest. Background technique [0002] Compared with indoor images, the use of depth equipment to capture outdoor scenes is more affected by the environment. Outdoor atmosphere, light, heavy fog and other environments will have a greater impact on depth acquisition equipment. Depth devices based on structured light such as Kinect cannot be used outdoors, and the cost of devices such as lidar that can be used outdoors is too high, and the use of binocular camera stereo matching methods to estimate depth has a large amount of calculations. The low area effect is not good. In addition, self-driving cars, SLAM and other fields have great requirements for the three-dimensional structure of outdoor scenes, so monocular depth estimation based on outdoor images is a problem worthy ...

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
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06T7/41G06T7/529
CPCG06T7/41G06T7/529G06T2207/10028G06V20/38G06F18/213G06F18/22G06F18/24323
Inventor 喻莉张蓥
Owner HUAZHONG UNIV OF SCI & TECH
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com