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Part2Point:一个以部分为导向的点云重建框架

Yu-Cheng Feng¹, Sheng-Yun Zeng¹, Tyng-Yeu Liang¹

¹Department of Electrical Engineering, National Kaohsiung University of Science and Technology, No. 415, Jiangong Road, Sanmin District, Kaohsiung City 807618, Taiwan.

Sensors (Basel, Switzerland)

|January 11, 2024

概括

本研究介绍了Part2Point,这是一个用于3D对象建模的新框架,该框架可以逐部分重建模型. 这种方法显著降低了计算成本,并增强了虚拟和增强现实应用程序的模型细节.

科学领域:

计算机视觉计算机视觉
计算机图形计算机图形
几何建模几何建模

背景情况:

3D对象建模对于虚拟和增强现实 (VR/AR) 是至关重要的.
目前的方法,如手动编辑或激光雷达扫描,耗时且昂贵.
通过GPU加速的深度学习模型可以从二维图像中生成3D模型,但由于计算需求,它们在高分辨率方面遇到了困难.

研究的目的:

解决现有的3D模型生成技术的计算和内存低效.
提出一个新的框架,提高高分辨率3D模型重建的效率和可行性.
为了使VR/AR应用程序能够生成更详细的3D模型.

主要方法:

提出了一个以部分为导向的点云重建框架,命名为Part2Point.
该框架将对象分成部分,为每个部分单独重建点云,然后将它们合并.
这种方法优化了相对于模型分辨率的参数使用.

主要成果:

Part2Point显著减少了用于高分辨率3D模型所需的学习网络参数的数量.
该框架最大限度地减少了计算时间和内存空间需求.
在重建的点云中实现了更好的分辨率,允许对象部分更详细.

结论:

与现有的方法相比,Part2Point为3D对象建模提供了一个更有效,更可扩展的解决方案.
面向部分的方法有效地克服了深度学习中高分辨率3D重建的局限性.
该框架提高了为高级应用程序创建详细和高保真3D模型的可行性.

关键词:

3D建模是什么 3D建模是什么人工智能的人工智能是人工智能.高分辨率的高分辨率解决方案参数数量是指参数的数量.部分细分部分细分部分.一个点云,一个点云.

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