基于流模型的三维点云生成方法

3D Point Cloud Generation Method Based on Flow Model

随着三维点云数据在计算机视觉任务的逐渐流行,合成或重建高分辨率、高保真点云的能力变得至关重要。虽然深度学习模型最近在点云识别和点云分类任务中取得了成功,但点云生成任务还困难重重。本文提出了一种基于流模型的点云生成模型,使用深度学习技术训练好该模型之后,只需要从简单的高斯分布随机采样数据,然后通过我们的模型就可以产生全新的高质量的点云形状。基于我们模型生成的点云的质量比大多数现存的模型都要好,可以为其他一些任务提供很好的先验点云,比如三维重建、点云补全任务。

With the gradual popularity of 3D point cloud in computer vision tasks,the ability of high-fidelity point cloud to be syn⁃thesized or reconstructed gradually.Although the deep learning model has recently proposed a stream model-based point cloud generation model in point cloud recognition and point cloud classification tasks,after training the model using deep learning tech⁃nology,it only needs to randomly sample data from a simple Gaussian distribution.Then we can generate a new estimated point cloud shape through our model.The quality of the point cloud generated based on our model is better than most existing models.It can provide a good prior point cloud for some other tasks,such as 3D reconstruction,and point cloud completion tasks.