Panorama-based self-supervised learning scene point cloud completion data set generation method
A supervised learning and point cloud completion technology, applied in the field of 3D reconstruction, can solve problems such as difficult to reconstruct real point cloud scenes, difficult to obtain data, and lack of integrity
Active Publication Date: 2021-12-17
DALIAN UNIV OF TECH
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AI-Extracted Technical Summary
Problems solved by technology
The invention belongs to the technical field of three-dimensional reconstruction in the field of computer vision, and provides a panorama-based self-supervised learning scene point cloud completion data set generation method. A panoramic RGB image, a panoramic depth map and a panoramic normal map under the same viewpoint are used as input, and paired incomplete point clouds and target point clouds with RGB information and normal information can be generated to construct a self-supervised learning data set of a training ground scenic spot cloud complementation network. The key point of the invention is the processing of the stripe problem and the point-to-point shielding problem in the shielding prediction based on viewpoint conversion and the equirectangular projection and conversion process. According to the method, the acquisition mode of real scene point cloud data is simplified; a shielding prediction idea of viewpoint conversion; and a viewpoint selection strategy is designed.
Depth mapData set +6
- Experimental program(1)
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