Multi-Distance Point Cloud Quality Assessment

The popularity of smartphones, virtual reality headsets, and head-mounted devices is fomenting immersive applications that employ realistic representations of the real world. Among these, Point Cloud (PC) contents have recently gained prominence in academia and industry. However, there is some consensus that PC objective quality assessment methods are still an open problem. In this paper, we introduce a PC quality metric based on multiple distances between reference and test PCs. These distances are computed in both still points and texture spaces. Distances computed in still points space consider the direct differences between reference and test points. Distances in the texture space are measured after computing the Local Binary Pattern (LBP) descriptor of PCs. Since PCs are not equally geometrically distributed, we adapted the LBP descriptor to make the nearest points as the neighborhood pixels of the descriptor. The difference between the LBP statistics of reference and test PCs is used to assess the quality of the test PC. Experimental results show the proposed method performs well when compared with the state-of-the-art Point Cloud Quality Assessment (PCQA) methods.