Learning signed distance functions (SDFs) from 3D point clouds is an important task in 3D computer vision. However, without ground truth signed distances, point normals or clean point clouds, current methods still struggle from learning SDFs from noisy point clouds. To overcome this challenge, we propose to learn SDFs via a noise to noise mapping, which does not require any clean point cloud or ground truth supervision for training. Our novelty lies in the noise to noise mapping which can infer a highly accurate SDF of a single object or scene from its multiple or even single noisy point cloud observations. Our novel learning manner is supported by modern Lidar systems which capture multiple noisy observations per second. We achieve this by a novel loss which enables statistical reasoning on point clouds and maintains geometric consistency although point clouds are irregular, unordered and have no point correspondence among noisy observations. Our evaluation under the widely used benchmarks demonstrates our superiority over the state-of-the-art methods in surface reconstruction, point cloud denoising and upsampling. Our code, data, and pre-trained models are available at https://github.com/mabaorui/Noise2NoiseMapping/

翻译：从三维点云中学习有符号距离函数（SDF）是三维计算机视觉领域的重要任务。然而，由于缺乏真值有符号距离、点法线或清洁点云，现有方法在从含噪点云学习SDF时仍面临挑战。为克服这一难题，我们提出通过噪声到噪声映射学习SDF，该方法无需任何清洁点云或真值监督信号进行训练。我们的创新点在于噪声到噪声映射，它能从单一物体或场景的多个（甚至单个）含噪点云观测中推断出高精度的SDF。这一新颖的学习范式得益于现代激光雷达系统每秒捕获多个含噪观测数据的能力。我们通过设计新型损失函数实现该目标：该损失函数能够对点云进行统计推理，并在点云存在不规则性、无序性及观测间无点对应关系的情况下，保持几何一致性。在广泛使用的基准数据集上的评估表明，我们在表面重建、点云去噪与上采样任务上均优于现有最先进方法。我们的代码、数据集及预训练模型已开源至 https://github.com/mabaorui/Noise2NoiseMapping/