A distributed source autoencoder of local visual descriptors for 3D reconstruction

Abstract

• Application of a deep learning to the compression of local visual descriptors. • Combination of distributed source coding and autoencoders. • Definition of a low-complexity encoder suitable for mobile low-cost boards. • Competitive compression and reconstruction performances w.r.t existing solutions. • Optimization of the coding scheme for 3D scene reconstruction applications. This paper presents a local descriptor coding scheme for multicamera surveillance and 3D reconstruction embedding an autoencoder into a traditional distributed source coding strategy. The proposed solution permits shifting most of the computational complexity at the decoder/receiver and exploiting the correlation among descriptors of different cameras (thus reducing the coded bit rate) without increasing the inter-device communication load. Experimental results show that the proposed scheme permits obtaining a satisfying accuracy with respect to the most recent solutions while generating a limited bit rate.