A novel framework called Squeeze3D uses pre-trained models to compress 3D data efficiently, achieving high compression ratios while maintaining visual quality.
We propose Squeeze3D, a novel framework that leverages implicit prior
knowledge learnt by existing pre-trained 3D generative models to compress 3D
data at extremely high compression ratios. Our approach bridges the latent
spaces between a pre-trained encoder and a pre-trained generation model through
trainable mapping networks. Any 3D model represented as a mesh, point cloud, or
a radiance field is first encoded by the pre-trained encoder and then
transformed (i.e. compressed) into a highly compact latent code. This latent
code can effectively be used as an extremely compressed representation of the
mesh or point cloud. A mapping network transforms the compressed latent code
into the latent space of a powerful generative model, which is then conditioned
to recreate the original 3D model (i.e. decompression). Squeeze3D is trained
entirely on generated synthetic data and does not require any 3D datasets. The
Squeeze3D architecture can be flexibly used with existing pre-trained 3D
encoders and existing generative models. It can flexibly support different
formats, including meshes, point clouds, and radiance fields. Our experiments
demonstrate that Squeeze3D achieves compression ratios of up to 2187x for
textured meshes, 55x for point clouds, and 619x for radiance fields while
maintaining visual quality comparable to many existing methods. Squeeze3D only
incurs a small compression and decompression latency since it does not involve
training object-specific networks to compress an object.