Method for 3D scene dense reconstruction based on monocular visual slam

US11210803B2 · kind B2 · utility

Assignee

• Dalian University of Technology · CN

Inventors

• Xinchen Ye · 东风镇, CN
• Wei Zhong · 东风镇, CN
• Zhihui Wang · Beijing, CN
• Haojie Li · 东风镇, CN
• Lin Lin · Longbeilingcun, CN
• Xin Fan · Irvine, US
• Zhongxuan Luo · 东风镇, CN

Key dates

• Filing date: Jan 7, 2019
• Grant date: Dec 28, 2021
• Expiry date: Feb 21, 2039

Classification

• Technology area (CPC G): Physics
• CPC primary: G06T2210/56
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

The present invention provides a method of dense 3D scene reconstruction based on monocular camera and belongs to the technical field of image processing and computer vision, which builds the reconstruction strategy with fusion of traditional geometry-based depth computation and convolutional neural network (CNN) based depth prediction, and formulates depth reconstruction model solved by efficient algorithm to obtain high-quality dense depth map. The system is easy to construct because of its low requirement for hardware resources and achieves dense reconstruction only depending on ubiquitous monocular cameras. Camera tracking of feature-based SLAM provides accurate pose estimation, while depth reconstruction model with fusion of sparse depth points and CNN-inferred depth achieves dense depth estimation and 3D scene reconstruction; The use of fast solver in depth reconstruction avoids solving inversion of large-scale sparse matrix, which improves running speed of the algorithm and ensures the real-time dense 3D scene reconstruction based on monocular camera.