Mesh Generation

New solutions have emerged that address the limitations of Marching Cubes, allowing the conversion of dense meshes to low-poly meshes.

Earlier in the course, we highlighted the significance of differentiability and how mesh rendering is non-differentiable, involving discrete decisions like:

• Is this pixel inside the triangle?

While this remains true, new research introduces a differentiable approach to mesh generation by treating mesh triangles as discrete symbols, similar to words in a language model.

MeshAnything

MeshAnything is a recent research project that converts dense meshes to low-poly meshes using techniques introduced in MeshGPT.

The main components of MeshAnything are:

1. VQ-VAE Encoder: Encodes dense 3D data to a discrete latent representation using a Vector Quantization (VQ) Variational Autoencoder (VAE).
2. Autoregressive Transformer Decoder: Generates the triangles of the mesh using an autoregressive transformer decoder.

More details can be found in the paper MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers.

Implications

MeshAnything represents a major step in 3D mesh generation, addressing a major bottleneck toward practical generative 3D tools.

However, the current results are comparable to or worse than traditional topology reduction methods like Decimate, still requiring significant manual refinement.

Differentiable mesh generation opens the door to context-aware topology reduction, i.e. accounting for shape and deformation. While still a work in progress, solving this will lead to highly practical 3D tools.