Consolidation of Low-quality Point Clouds from Outdoor Scenes
Article first published online: 19 AUG 2013
© 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and John Wiley & Sons Ltd.
Computer Graphics Forum
Volume 32, Issue 5, pages 207–216, August 2013
How to Cite
Wang, J., Xu, K., Liu, L., Cao, J., Liu, S., Yu, Z. and Gu, X. D. (2013), Consolidation of Low-quality Point Clouds from Outdoor Scenes. Computer Graphics Forum, 32: 207–216. doi: 10.1111/cgf.12187
- Issue published online: 19 AUG 2013
- Article first published online: 19 AUG 2013
- I.3.3 [Computer Graphics]: 3D point data—Outlier detection; Normal estimation; Noise smoothing; Feature preserving
The emergence of laser/LiDAR sensors, reliable multi-view stereo techniques and more recently consumer depth cameras have brought point clouds to the forefront as a data format useful for a number of applications. Unfortunately, the point data from those channels often incur imperfection, frequently contaminated with severe outliers and noise. This paper presents a robust consolidation algorithm for low-quality point data from outdoor scenes, which essentially consists of two steps: 1) outliers filtering and 2) noise smoothing. We first design a connectivity-based scheme to evaluate outlierness and thereby detect sparse outliers. Meanwhile, a clustering method is used to further remove small dense outliers. Both outlier removal methods are insensitive to the choice of the neighborhood size and the levels of outliers. Subsequently, we propose a novel approach to estimate normals for noisy points based on robust partial rankings, which is the basis of noise smoothing. Accordingly, a fast approach is exploited to smooth noise, while preserving sharp features. We evaluate the effectiveness of the proposed method on the point clouds from a variety of outdoor scenes.