拉普达:隐性空间点云与适应性无关
IEEE transactions on visualization and computer graphics
|November 21, 2025
概括
点云无声化面临着与现实世界噪音失调的挑战. 拉普达 (Latent-space Point cloud Denoising with Adaptivity) 通过自适应地模拟隐性空间中的噪声并逐渐去除它来提高稳定性.
科学领域:
- 计算机图形 计算机图形
- 机器学习 机器学习
背景情况:
- 消除点云是至关重要的,但也很困难.
- 由于与合成训练数据的不对齐,当前的方法与现实世界的噪音作斗争.
研究的目的:
- 引入LaPDA (具有适应性的潜空间点云去除) 来解决噪声错位问题.
- 提高点云消除算法的稳定性.
主要方法:
- 隐性空间中的自适应噪声建模,以对齐或调整噪声分布.
- 逐渐消除噪音,优化点的空间分布.
- 基于受控合成噪声的培训目标.
主要成果:
- 在合成和扫描数据集上,LaPDA表现出更好的准确性和稳定性.
- 性能优于现有的最先进的消毒方法.
结论:
- 在点云消噪中,LaPDA有效地减轻了噪声失调.
- 为现实世界杂点云数据提供更强大的解决方案.
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