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There has been significant progress in improving the accuracy and quality ofconsumer-level dense depth sensors. Nevertheless, there remains a common depthpixel artifact which we call smeared points. These are points not on any 3Dsurface and typically occur as interpolations between foreground and backgroundobjects. As they cause fictitious surfaces, these points have the potential toharm applications dependent on the depth maps. Statistical outlier removalmethods fare poorly in removing these points as they tend also to remove actualsurface points. Trained network-based point removal faces difficulty inobtaining sufficient annotated data. To address this, we propose a fullyself-annotated method to train a smeared point removal classifier. Our approachrelies on gathering 3D geometric evidence from multiple perspectives toautomatically detect and annotate smeared points and valid points. To validatethe effectiveness of our method, we present a new benchmark dataset: the RealAzure-Kinect dataset. Experimental results and ablation studies show that ourmethod outperforms traditional filters and other self-annotated methods. Ourwork is publicly available athttps://github.com/wangmiaowei/wacv2024_smearedremover.git.

Self-Annotated 3D Geometric Learning for Smeared Points Removal