神经TPS:从单个稀疏点云中学习没有priors的标记距离函数
IEEE transactions on pattern analysis and machine intelligence
|October 7, 2024
概括
这项研究引入了一种新的神经网络,用于从稀疏点云中进行3D表面重建. 该方法提高了准确性和概括性,而不依赖于先前的知识或正常数据.
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:
- 从点云的表面重建在3D计算机视觉中至关重要.
- 目前的方法因依赖大规模监督和学到的先验而扎着几何变化和稀疏的数据.
- 对未见的几何变化进行概括,特别是来自极其稀疏的点云,仍然是一个挑战.
研究的目的:
- 开发一个神经网络,从单个稀疏点云直接推断SDF.
- 通过避免签名远程监督,学习先验和正常数据来克服现有方法的局限性.
- 提高对稀疏和看不见点云的表面重建的概括能力和准确性.
主要方法:
- 一个新的神经网络直接从稀疏的点云中推断SDF,而无需事先的知识或正常数据.
- 表面参数化和SDF推断是从端到端学习的.
- 参数化表面被用作粗采样器,用于生成基于薄板线 (TPS) 网络的监督,从而实现统计SDF推断.
主要成果:
- 拟议的方法显著提高了对未见的点云的概括能力和准确性.
- 实验结果表明,在合成和现实世界的稀疏点云数据集上,与最先进的方法相比,性能优越.
- 该方法有效地解决了表面重建中数据稀疏性所带来的挑战.
结论:
- 开发的神经网络提供了一个强大的解决方案,用于从稀疏点云的表面重建.
- 表面参数化和SDF推断的端到端学习,再加上一种新的监督采矿策略,证明了其有效性.
- 这项工作推进了处理稀疏数据的最先进技术,以实现准确和可概括的3D表面重建.
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