3D Reconstruction and Object Detection for HoloLens

Zequn Wu, Tianhao Zhao, Chuong V. Nguyen
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引用次数: 5

Abstract

Current smart glasses such as HoloLens excel at positioning within the physical environment, however object and task recognition are still relatively primitive. We aim to expand the available benefits of MR/AR systems by using semantic object recognition and 3D reconstruction. Particularly in this preliminary study, we successfully use a HoloLens to build 3D maps, recognise and count objects in a working environment. This is achieved by offloading these computationally expensive tasks to a remote GPU server. To further achieve realtime feedback and parallelise tasks, object detection is performed on 2D images and mapped to 3D reconstructed space. Fusion of multiple views of 2D detection is additionally performed to refine 3D object bounding boxes and separate nearby objects.
HoloLens的三维重建和目标检测
目前的智能眼镜如HoloLens擅长在物理环境中定位,但物体和任务识别仍然相对原始。我们的目标是通过使用语义对象识别和3D重建来扩大MR/AR系统的可用优势。特别是在这项初步研究中,我们成功地使用HoloLens来构建3D地图,识别和计数工作环境中的物体。这是通过将这些计算成本高昂的任务卸载到远程GPU服务器来实现的。为了进一步实现实时反馈和并行任务,在二维图像上进行目标检测,并将其映射到三维重构空间。此外,还对2D检测的多个视图进行融合,以细化3D对象边界框并分离附近的对象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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