Self-Annotated 3D Geometric Learning for Smeared Points Removal
Miaowei Wang Daniel Morris
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.