PCGen:一个完全可并行的点云生成模型
Nicolas Vercheval1,2, Remco Royen3, Adrian Munteanu3
1Research Group for Artificial Intelligence and Sparse Modelling (GAIM), Department of Telecommunications and Information Processing, Faculty of Engineering and Architecture, Ghent University, 9000 Ghent, Belgium.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
我们开发了一种快速的向量量子化变量自编码器 (VQVAE),可以在毫秒内生成3D点云,从而实现扩展现实和快速原型化中的实时应用.
科学领域:
- 计算机视觉 计算机视觉
- 3D 图形 3D 图形
- 机器学习 机器学习
背景情况:
- 实时计算对于3D扩展现实应用程序至关重要.
- 目前的点云生成方法太慢,无法实时使用.
- 生成型模型为革命性的3D扩展现实提供了潜力.
研究的目的:
- 介绍一种新的VQVAE模型,用于快速,高质量的点云合成.
- 解决增强现实和虚拟现实中的实时计算瓶.
- 为条件生成和数据探索提供一个紧的表示.
主要方法:
- 开发了一个VQVAE,在建筑上进行了改进,以提高强度,效率和重建质量.
- 在解码器中集成了创新的计算层,注意力机制和错误过器.
- 引入了使用双编码系统进行快速推断的并行采样策略.
主要成果:
- 在毫秒内合成了高质量的点云,比现有方法快得多.
- 实现了适合各种下游任务的紧样本表示.
- 证明了个别建筑和采样贡献的有效性.
结论:
- 拟议的VQVAE模型满足了3D扩展现实的实时需求.
- 模型的紧表示方便条件生成和快速原型制作.
- 这项工作推进了用于高效3D数据合成的生成模型.
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