  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.
