通过噪音对噪音映射,从噪音点云到噪音映射,快速学习签名距离函数
概括
这项研究引入了一种新的噪声对噪声映射,用于从杂的3D点云中学习符号距离函数 (SDF). 它可以在没有地面真相的情况下实现准确的SDF,改善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
- 计算几何学的计算几何学
背景情况:
- 从点云中学习符号距离函数 (SDF) 对3D重建至关重要.
- 现有的方法与杂的点云作斗争,缺乏地面真相距离或正常值.
- 这限制了SDF学习在现实世界的场景中的准确性和稳定性.
研究的目的:
- 开发一种可靠的方法来学习SDF从没有地面真相监督的杂点云.
- 为了从对象或场景的单个或多个噪音观测中进行准确的SDF推断.
- 为了提高3D计算机视觉任务的性能,如表面重建和点云处理.
主要方法:
- 一种新的噪声对噪声映射方法用于SDF学习.
- 一个新的损失函数,允许在不规则的点云上进行统计推理和几何一致性.
- 在CUDA中实现的多分辨率哈希编码用于加速训练.
- 使用SDF作为一个先验的多视图重建的新方案.
主要成果:
- 从杂的点云中准确的SDF推断,即使是单个观测.
- 由于CUDA加速哈希编码,训练时间显著减少 (十倍).
- 与最先进的方法相比,在表面重建,点云消除和升级样本方面的性能优越.
- 在广泛使用的基准指标上表现出有效性.
结论:
- 拟议的噪声对噪声映射有效地从不完美的3D数据中学习SDF.
- 该方法为3D计算机视觉任务提供了强大而高效的解决方案.
- 这项工作推进了从杂的点云和多视图重建中学习的最先进技术.
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