Recognition-Aware Deep Video Compression For Remote Surveillance

The objective of 3-dimensional point cloud completion is to estimate a plausible complete shape from a given partial point cloud. Most of the data-driven point cloud completion approaches have been proposed in a supervised manner needing one-to-one correspondence between the partial and complete shapes. A promising way to solve the paired data dependency is to use the mapping capability of a pre-trained point cloud generation network to the best possible matching latent vector. However, recovering the composite structural details and complex geometry of a 3D shape is often difficult using a single latent vector alone. in this paper, we propose to employ multiple latent vectors, each of which generates individual feature maps, which are then combined to reconstruct a faithful complete 3D shape corresponding to an available partial shape. Deploying more than one latent vector enables the pre-trained generative network to increase its fidelity by using multiple combinations of feature representations learned by each single latent. Experimental results show that our algorithm performs well compared to the other existing shape completion methods. We also study the completion performance with a varying number of latent codes and the role of each latent vector in the final complete shape generation.