DUALFEAT: Dual Feature Aggregation For Video Object Detection

Point cloud is an informative type of media to represent objects and scenes. Along with the geometry information that records the 3D shapes, multiple types of attribute information can be included to characterize the visual appearance of objects and scenes. To deal with the massive data volume of point clouds, developing compression algorithms is necessary. While various prediction techniques have been proposed to reduce the information redundancy for individual point cloud attribute, the correlation between different types of attribute is, however, not fully investigated. This paper first studies the correlation between different types of attribute. A cross-type attribute prediction method is then proposed. The proposed method is evaluated on top of different point cloud compression frameworks with various point cloud sequences. Experimental results show that the proposed cross-type attribute prediction method is able to significantly improve the coding efficiency with marginal coding complexity.