Key frame extraction method for RGBD 3D reconstruction

Abstract

The invention discloses a key frame extraction method for RGBD 3D reconstruction. The method includes at first, dividing a plurality of data frames that are temporally adjacent to each other into a group for the RGBD data flow acquired by a camera and the camera pose estimated by a visual odometer; and for each group of data, projecting each frame of depth image to a first frame of depth image according to the camera pose and the camera parameters; projecting each group of first frame of RGB image to the remaining frames of image to obtain the gray value of each projected RGB image by linear interpolation; estimating the blur degree of each frame of RGB image, and combining with the weight of the corresponding projected depth image to obtain the weight of the projected RGB image; and fusing the intra-group projected depth images and the projected RGB images according to the obtained weights to obtain the RGBD key frame. The key frame extraction method reduces the void and noise of data acquired by a depth camera to obtain clear depth images and RGB images, and provides reliable data source for other works in the 3D reconstruction such as the camera pose global optimization and texture extraction.

Description

technical field [0001] The invention belongs to the field of computer vision and computer graphics and image processing, and specifically relates to a method for extracting key frames in an RGBD data stream, which provides research on camera pose estimation optimization and texture reconstruction in three-dimensional reconstruction based on RGBD data streams. In order to obtain a more reliable data source, it is of great significance to study the 3D reconstruction technology based on RGBD data. Background technique [0002] With the popularity of depth sensors and the development of 3D reconstruction technology, research on 3D model reconstruction based on RGBD data is emerging in recent years. Compared with the traditional 3D reconstruction based on RGB images, the depth image provides the 3D information of the scene, which greatly improves the feasibility and accuracy of 3D reconstruction. Keyframe extraction plays an important role in camera pose estimation, camera reloc...

AI Technical Summary

Problems solved by technology

[0004] The above key frame extraction methods usually face the problem of low quality of key frames, such as noise and holes in the depth image, and motion blur in the RGB image, etc., which have a certain impact on the optimization of the camera pose and texture extraction.

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