Learned Compression of Point Cloud Geometry and Attributes in a Single Model through Multimodal Rate-Control

TL;DR

This paper introduces a single adaptive autoencoder model for joint compression of point cloud geometry and attributes, enabling efficient, view-dependent, and locally conditioned compression with comparable quality to state-of-the-art methods.

Contribution

It proposes a unified autoencoder approach for joint geometry and attribute compression, replacing separate models and enabling local quality-rate conditioning.

Findings

Achieves comparable compression performance to state-of-the-art methods.

Reduces complexity by using a single model for both modalities.

Supports view-dependent and local quality adjustments.

Abstract

Point cloud compression is essential to experience volumetric multimedia as it drastically reduces the required streaming data rates. Point attributes, specifically colors, extend the challenge of lossy compression beyond geometric representation to achieving joint reconstruction of texture and geometry. State-of-the-art methods separate geometry and attributes to compress them individually. This comes at a computational cost, requiring an encoder and a decoder for each modality. Additionally, as attribute compression methods require the same geometry for encoding and decoding, the encoder emulates the decoder-side geometry reconstruction as an input step to project and compress the attributes. In this work, we propose to learn joint compression of geometry and attributes using a single, adaptive autoencoder model, embedding both modalities into a unified latent space which is then entropy encoded. Key to the technique is to replace the search for trade-offs between rate, attribute quality and geometry quality, through conditioning the model on the desired qualities of both modalities, bypassing the need for training model ensembles. To differentiate important point cloud regions during encoding or to allow view-dependent compression for user-centered streaming, conditioning is pointwise, which allows for local quality and rate variation. Our evaluation shows comparable performance to state-of-the-art compression methods for geometry and attributes, while reducing complexity compared to related compression methods.