LAUSR

With the rapid advancement of networks, graphics hardware, and computing techniques, the 3D information has been widely applied in various domains, such as virtual reality and medical industry. The proliferation of such applications has produced explosively growing 3D multimedia data, which lead to the requirement for large-scale 3D data analysis. The traditional 3D feature representation and learning algorithms may not work well for large-scale analysis, such as 3D retrieval and recognition, since the computational cost in such cases is much larger. How to design efficient and effective 3D feature representation and learning techniques to deal with the 3D big data is desirable and meaningful. This special issue targets the most recent technical progress on the analysis and applications of large-scale 3D multimedia. Submissions came from an open call for papers and with the assistance of professional referees. Eight papers are finally selected out from in total 24 submissions after two rounds of rigorous reviews. These accepted papers cover several popular topics of large-scale 3D multimedia analysis and applications, including 3D recognition, retrieval, compression, preprocessing, etc. We summarize these papers as follows: In B3D skeleton based action recognition by video-domain translation-scale invariant mapping and multi-scale dilated CNN^ [4], Li et al. propose an action recognition approach from 3D skeleton videos. By a video domain translation-scale invariant image mapping, the 3D skeleton videos are transformed to skeleton color images, which are input to a multi-scale Multimed Tools Appl https://doi.org/10.1007/s11042-018-6359-9