There has been significant progress in improving the accuracy and quality of consumer-level dense depth sensors. Nevertheless, there remains a common depth pixel artifact which we call smeared points. These are points not on any 3D surface and typically occur as interpolations between foreground and background objects. As they cause fictitious surfaces, these points have the potential to harm applications dependent on the depth maps. Statistical outlier removal methods fare poorly in removing these points as they tend also to remove actual surface points. Trained network-based point removal faces difficulty in obtaining sufficient annotated data. To address this, we propose a fully self-annotated method to train a smeared point removal classifier. Our approach relies on gathering 3D geometric evidence from multiple perspectives to automatically detect and annotate smeared points and valid points. To validate the effectiveness of our method, we present a new benchmark dataset: the Real Azure-Kinect dataset. Experimental results and ablation studies show that our method outperforms traditional filters and other self-annotated methods. Our work is publicly available at https://github.com/wangmiaowei/wacv2024_smearedremover.git.

翻译：在提升消费级深度传感器精度与质量方面已取得显著进展。然而，仍存在一种常见的深度像素伪影，我们称之为涂抹点。这些点并不位于任何三维表面上，通常表现为前景与背景物体之间的插值。由于会生成虚假表面，这些点可能对依赖深度图的应用造成损害。统计离群点去除方法在消除这些点时效果欠佳，因其往往会同时移除实际表面点。基于训练网络的点去除方法则面临标注数据不足的难题。为解决这一问题，我们提出了一种完全自标注的方法来训练涂抹点去除分类器。该方法通过从多视角收集三维几何证据，自动检测并标注涂抹点与有效点。为验证有效性，我们提出了新型基准数据集：真实Azure-Kinect数据集。实验与消融研究表明，我们的方法优于传统滤波器及其他自标注方法。相关代码已开源：https://github.com/wangmiaowei/wacv2024_smearedremover.git。