Neural Progressive Meshes
Yun-Chun Chen Vladimir G. Kim Noam Aigerman Alec Jacobson
The recent proliferation of 3D content that can be consumed on hand-held
devices necessitates efficient tools for transmitting large geometric data,
e.g., 3D meshes, over the Internet. Detailed high-resolution assets can pose a
challenge to storage as well as transmission bandwidth, and level-of-detail
techniques are often used to transmit an asset using an appropriate bandwidth
budget. It is especially desirable for these methods to transmit data
progressively, improving the quality of the geometry with more data. Our key
insight is that the geometric details of 3D meshes often exhibit similar local
patterns even across different shapes, and thus can be effectively represented
with a shared learned generative space. We learn this space using a
subdivision-based encoder-decoder architecture trained in advance on a large
collection of surfaces. We further observe that additional residual features
can be transmitted progressively between intermediate levels of subdivision
that enable the client to control the tradeoff between bandwidth cost and
quality of reconstruction, providing a neural progressive mesh representation.
We evaluate our method on a diverse set of complex 3D shapes and demonstrate
that it outperforms baselines in terms of compression ratio and reconstruction
quality.