可扩展软件组用于点云上的3D实例细分
IEEE transactions on pattern analysis and machine intelligence
|October 20, 2023
概括
软集团通过允许点属于多个类,减少错误和假阳性来增强3D实例细分. SoftGroup++通过优化k-Nearest Neighbor模块进一步提高了大型场景的可扩展性.
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:
- 当前的3D实例细分方法依赖于硬语义预测,导致错误传播和糟糕的结果.
- 由于计算瓶,现有的快速方法不适合实时应用,特别是在大规模场景中.
研究的目的:
- 开发一个准确和可扩展的3D实例细分网络.
- 解决现有方法的局限性,包括错误传播和计算效率低下.
主要方法:
- 软集团允许点与多个类相关联,减轻语义预测不确定性.
- 软集团++优化了k-近邻 (k-NN) 模块,使用k-NN octree,类意识的金字塔缩放和晚期devoxelization来提高可扩展性.
- 这些方法通过学习将它们归类为背景来抑制假阳性.
主要成果:
- 软集团和软集团++在AP50中超过了最先进的基线6%-16%的水平.
- 与SoftGroup相比,SoftGroup++在大型场景中平均实现了6倍的加速度.
- 软集团框架展示了多功能性,改进了对象检测和全视分段.
结论:
- 软集团通过处理语义不确定性,为准确的3D实例细分提供了强大的解决方案.
- 软集团++显著提高了可扩展性,使实时3D实例细分成为大规模环境的可行性.
- 提出的方法提供了适用于各种3D计算机视觉任务的一般框架.
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