Sparse multi-view-angle three-dimensional reconstruction method for indoor scene

Abstract

The invention belongs to the field of computer vision and computer graphics, and aims to ensure the universality of the application of the technology and generate a relatively accurate three-dimensional model. The technical scheme adopted by the invention is as follows: the sparse multi-view three-dimensional reconstruction method for the indoor scene comprises the following steps of: obtaining depth information and semantic information based on a deep learning method, and then realizing model fusion of each sparse view angle by using a global-local registration method, wherein the global-local registration method specifically comprises the steps of filtering and smoothing the depth map by using a method of establishing a patch by using a single view angle, converting the depth map under each view angle into point cloud and carrying out fusion. The method is mainly applied to image processing occasions.

Description

technical field [0001] The invention belongs to the fields of computer vision and computer graphics, in particular to a method for three-dimensional reconstruction of an indoor scene under sparse multi-angle shooting. Background technique [0002] With the increasing demand for indoor navigation, home or office design, and augmented reality, 3D reconstruction and understanding of indoor scenes has become a hot topic in computer vision and graphics. Today's reconstruction methods broadly fall into two categories. The first method is to use a time-of-flight or structured light-based depth camera to scan the scene, KinectFusion (R.A. Newcombe, S.Izadi, O.Hilliges, D.Molyneaux, D.Kim, A.J.Davison, P.Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon. KinectFusion: Real-time densesurface mapping and tracking. In ISMAR, pages 127–136, 2011.) showed in detail the process of using Kinect for indoor 3D reconstruction, after which ElasticFusion (T.Whelan, R.F.Salas-Moreno, B.Glocker, A....

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Problems solved by technology

Simultaneous Localization and Map Building (SLAM) (M.G. Dissanayake, P. Newman, S. Clark, H.F. Durrant Whyte, and M. Csorba. A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans. Robotics & Automation, 17(3): 229–241, 2001.) Technique and Structure of Motion Recovery (SFM) (N. Snavely, S.M. Seitz, and R. Szeliski. Photo tourism: exploring photocollections in 3D. ACM Transactions on Graphics, 25(3):835–846, 2006.) technology is two popular color image-based reconstruction methods, but these methods require rich textures in the scene to extract effective features. In addition, the above two methods require continuous frame tracking Or based on the collection of dense perspective, which limits the scope of application to a certain extent

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