AdaPoinTr:通过适应性几何感知变压器完成多元点云
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概括
本研究介绍了PoinTr和AdaPoinTr,这些基于变压器的模型用于点云完成. AdaPoinTr显著提高了复杂3D数据的训练效率和性能,并设定了新的最先进的结果.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 3D数据处理 3D数据处理
背景情况:
- 点云完成对于从不完整的数据重建3D对象至关重要.
- 现有的方法与复杂和多样化的现实世界点云场景作斗争.
- 变压器为改善结构知识和细节保存提供了潜力.
研究的目的:
- 开发一种基于变压器的高级架构,用于点云完成.
- 为了提高模型的效率和性能在处理复杂和杂的3D数据.
- 建立新的基准并对具有挑战性的多样化数据集评估方法.
主要方法:
- 建议PoinTr:用于设置到设置点云翻译的变压器编码器解码器架构.
- 引入了AdaPoinTr:结合了自适应查询生成和一个新的拒绝任务.
- 开发了几何感知块,以明确模拟本地几何关系.
- 扩展了对场景级点云完成的方法,使用了几何增强的框架.
主要成果:
- 通过AdaPoinTr,完成性能提高了20%以上,训练时间缩短了15倍.
- 在多个基准指标 (PCN,ShapeNet-55,KITTI) 上建立了新的最先进的结果,具有优越的指标.
- 在具有挑战性,多样化的点云数据集上表现出有效性,反映真实世界的场景.
- 与以前的领先方法相比,实现了更高的吞吐量和更少的FLOP.
结论:
- 像PoinTr和AdaPoinTr这样的变压器架构对于点云完成非常有效.
- 在3D数据重建的效率和性能方面,AdaPoinTr提供了显著的进步.
- 拟议的基准和场景级完成框架推动了点云研究的边界.
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