论文标题
fast-image2point：使用3D监督迈向单个图像的实时点云重建
Fast-Image2Point: Towards Real-Time Point Cloud Reconstruction of a Single Image using 3D Supervision
论文作者
论文摘要
3D重建问题中的一个关键问题是如何训练机器人或机器人以模型3D对象。在实时系统（例如自动驾驶汽车）中导航等许多任务直接取决于此问题。这些系统通常具有有限的计算能力。尽管近年来3D重建系统的进展很大，但由于现有方法的高复杂性和计算需求，将它们应用于自动驾驶汽车中的导航系统等实时系统仍然具有挑战性。这项研究解决了以更快（实时）方式重建单视图像中显示的对象的当前问题。为此，开发了一个简单而强大的深度神经框架。提出的框架由两个组件组成：特征提取器模块和3D发电机模块。我们将点云表示形式用于我们的重建模块的输出。将Shapenet数据集用于将方法与计算时间和准确性方面的现有结果进行比较。模拟证明了所提出的方法的出色性能。 索引术语现实时间3D重建，单视重建，监督学习，深神经网络
A key question in the problem of 3D reconstruction is how to train a machine or a robot to model 3D objects. Many tasks like navigation in real-time systems such as autonomous vehicles directly depend on this problem. These systems usually have limited computational power. Despite considerable progress in 3D reconstruction systems in recent years, applying them to real-time systems such as navigation systems in autonomous vehicles is still challenging due to the high complexity and computational demand of the existing methods. This study addresses current problems in reconstructing objects displayed in a single-view image in a faster (real-time) fashion. To this end, a simple yet powerful deep neural framework is developed. The proposed framework consists of two components: the feature extractor module and the 3D generator module. We use point cloud representation for the output of our reconstruction module. The ShapeNet dataset is utilized to compare the method with the existing results in terms of computation time and accuracy. Simulations demonstrate the superior performance of the proposed method. Index Terms-Real-time 3D reconstruction, single-view reconstruction, supervised learning, deep neural network
