The goal of this paper is to assess the impact of noise in 3D camera-captured data by modeling the noise of the imaging process and applying it on synthetic training data. We compiled a dataset of specifically constructed scenes to obtain a noise model. We specifically model lateral noise, affecting the position of captured points in the image plane, and axial noise, affecting the position along the axis perpendicular to the image plane. The estimated models can be used to emulate noise in synthetic training data. The added benefit of adding artificial noise is evaluated in an experiment with rendered data for object segmentation. We train a series of neural networks with varying levels of noise in the data and measure their ability to generalize on real data. The results show that using too little or too much noise can hurt the networks' performance indicating that obtaining a model of noise from real scanners is beneficial for synthetic data generation.

翻译：本文旨在通过建模成像过程的噪声并将其应用于合成训练数据，评估噪声对3D相机采集数据的影响。我们构建了一个由特定场景组成的数据集以获取噪声模型。我们分别对影响图像平面内捕获点位置的横向噪声，以及影响垂直于图像平面轴向上点位置的轴向噪声进行建模。所估计的模型可用于模拟合成训练数据中的噪声。通过一项基于渲染数据的物体分割实验，评估了添加人工噪声的额外收益。我们训练了一系列包含不同噪声级别的神经网络，并测量了它们在真实数据上的泛化能力。结果表明，噪声过少或过多均会损害网络性能，说明从真实扫描仪获取噪声模型对合成数据生成具有积极作用。