LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks

We propose the first learned compression framework, LVAC, for volumetric functions represented by implicit networks -- a.k.a. coordinate-based networks (CBNs). In order to evaluate LVAC and compare it with prior (traditional) methods, we specifically focus on compressing point cloud attributes since there are no compression baselines for other signals' CBN-based representations. LVAC serves as the first baseline for them. More concretely, we consider the attributes of a point cloud as samples of a vector-valued volumetric function at discrete positions. To compress the attributes given the positions, we compress the parameters of the volumetric function. We represent the volumetric function by shifts of a CBN, or implicit neural network. Inputs to the network include both spatial coordinates and a latent vector per shift. To compress the latent vectors, we perform an end-to-end training of the overall pipeline where the latent vectors are rate-distortion optimized by back-propagation through a rate-distortion Lagrangian loss in an auto-decoder configuration. The result outperforms the current standard, RAHT, by 2--4 dB.