State-of-the-art intelligent versatile applications provoke the usage of full 3D, depth-based streams, especially in the scenarios of intelligent remote control and communications, where virtual and augmented reality will soon become outdated and are forecasted to be replaced by point cloud streams providing explorable 3D environments of communication and industrial data. One of the most novel approaches employed in modern object reconstruction methods is to use a priori knowledge of the objects that are being reconstructed. Our approach is different as we strive to reconstruct a 3D object within much more difficult scenarios of limited data availability. Data stream is often limited by insufficient depth camera coverage and, as a result, the objects are occluded and data is lost. Our proposed hybrid artificial neural network modifications have improved the reconstruction results by 8.53% which allows us for much more precise filling of occluded object sides and reduction of noise during the process. Furthermore, the addition of object segmentation masks and the individual object instance classification is a leap forward towards a general-purpose scene reconstruction as opposed to a single object reconstruction task due to the ability to mask out overlapping object instances and using only masked object area in the reconstruction process.

中文翻译：

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使用扩展的YOLOv3网络从不完美的深度数据重建3D对象。

最先进的智能通用应用程序激发了基于3D深度的完整流的使用，特别是在智能远程控制和通信的情况下，虚拟和增强现实将很快过时，并预计将被点代替云流提供可通信的3D环境和工业数据。现代对象重建方法中采用的最新颖的方法之一是使用对要重建的对象的先验知识。我们的方法有所不同，因为我们努力在数据可用性有限的更加困难的情况下重建3D对象。数据流通常受深度相机覆盖范围不足的限制，结果，对象被遮挡并且数据丢失。我们提出的混合人工神经网络修改方案将重构结果提高了8.53％，这使我们能够更精确地填充被遮挡的物体侧面并减少过程中的噪音。此外，对象分割蒙版和单个对象实例分类的添加是向通用场景重建的飞跃，这与单个对象重建任务相反，这是因为它能够掩盖重叠的对象实例并仅使用对象中的蒙版对象区域。重建过程。