CAP-UDF:从原始点云逐渐学习未签名距离函数,使用一致性意识的现场优化
概括
本研究介绍了CAP-UDF,这是一种从点云进行表面重建的新方法. 它学习一致性意识的无符号距离函数 (UDF),以准确地表示开放的表面,改进3D计算机视觉任务.
科学领域:
- 3D 计算机视觉 3D 计算机视觉
- 几何深度学习 几何深度学习
- 表面重建 表面重建
背景情况:
- 当前的表面重建方法通常依赖于已签名的距离函数,将其限制在封闭的表面上.
- 无符号距离函数 (UDF) 可以表示开放的表面,但由于点云不连续性,它们难以平滑.
- 现有的UDF学习方法在从原始点云中产生平滑的距离场面方面面临着挑战.
研究的目的:
- 提出CAP-UDF,一种用于从原始点云中学习一致性感知无符号距离函数 (UDF) 的新方法.
- 为了能够准确地重建开放和关闭的表面.
- 为了提高点云数据的学习距离场的流性和准确性.
主要方法:
- 通过对查询点强制执行字段一致性约束来学习一致性意识的UDF.
- 训练神经网络以动态推断查询到表面的关系,以进行渐进的表面估计.
- 使用基于学习的UDF梯度的新型多边化算法进行表面提取.
主要成果:
- CAP-UDF从各种点云来源 (原始,扫描,深度地图) 的表面重建中显示出显著的改进.
- 与现有的表面重建技术相比,该方法实现了最先进的性能.
- 扩展的实验显示了在无监督的点正常估计中具有竞争力的性能.
结论:
- CAP-UDF有效地解决了以前基于UDF的表面重建方法的局限性.
- 提出的一致性意识的学习方法产生了更准确和更光滑的表面表示.
- CAP-UDF为涉及点云表面重建的3D计算机视觉任务提供了强大而通用的解决方案.
相关概念视频
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01:18Distance Measurements by Taping
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01:15Distance Corrections
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01:26Influence of Earth's Curvature and Atmospheric Refraction on Leveling
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01:27Latitudes and Departures
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01:17Common Leveling Mistakes and Errors
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