Spatial scalability is an important functionality for point cloud compression. The current design of geometry reconstruction for spatial scalability applies the points at the center of nodes, ignoring correlations among neighbour nodes. In this work, a geometry reconstruction method based on neighbour occupancies is proposed, where the distribution of real points in the current node is predicted using the information of neighbour occupancies. In comparison to the state-of-the-art geometry-based point cloud compression, i.e., G-PCC, performance improvement of 1.15dB in D1- PSNR and 3.80dB in D2-PSNR in average, can be observed using proposed method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.