16 March 2018 Multiview point clouds denoising based on interference elimination
Author Affiliations +
J. of Electronic Imaging, 27(2), 023009 (2018). doi:10.1117/1.JEI.27.2.023009
Abstract
Newly emerging low-cost depth sensors offer huge potentials for three-dimensional (3-D) modeling, but existing high noise restricts these sensors from obtaining accurate results. Thus, we proposed a method for denoising registered multiview point clouds with high noise to solve that problem. The proposed method is aimed at fully using redundant information to eliminate the interferences among point clouds of different views based on an iterative procedure. In each iteration, noisy points are either deleted or moved to their weighted average targets in accordance with two cases. Simulated data and practical data captured by a Kinect v2 sensor were tested in experiments qualitatively and quantitatively. Results showed that the proposed method can effectively reduce noise and recover local features from highly noisy multiview point clouds with good robustness, compared to truncated signed distance function and moving least squares (MLS). Moreover, the resulting low-noise point clouds can be further smoothed by the MLS to achieve improved results. This study provides the feasibility of obtaining fine 3-D models with high-noise devices, especially for depth sensors, such as Kinect.
© 2018 SPIE and IS&T
Yang Hu, Qian Wu, Le Wang, Huanyu Jiang, "Multiview point clouds denoising based on interference elimination," Journal of Electronic Imaging 27(2), 023009 (16 March 2018). https://doi.org/10.1117/1.JEI.27.2.023009 Submission: Received 9 August 2017; Accepted 23 February 2018
Submission: Received 9 August 2017; Accepted 23 February 2018
JOURNAL ARTICLE
17 PAGES


SHARE
RELATED CONTENT


Back to Top