Abstract
Current consumer depth sensors produce depth maps that are often noisy and lack sufficient detail. Enhancing the quality of the 3D depth data obtained from compact depth Kinect-like sensors is an increasingly popular research area. Although depth data is known to carry a signal-dependent noise, the state-of-the-art denoising methods tend to employ denoising techniques which are independent of the depth signal itself. In this paper, we present a novel adaptive denoising filter to enhance object recognition from 3D depth data. We evaluate the performance of our proposed denoising filter against other state-of-the-art filters based on the enhancement of object recognition accuracy achieved after denoising the raw data with each filter. In order to perform object recognition from depth data, we make use of Differential Histogram of Normal Vectors (DHONV) features along with a linear SVM. Experiments show that our proposed filter outperformed the state-of-the-art de-noising methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.