Overview of meshes and voxels
As mentioned earlier in this book, meshes and voxels are two different 3D data representations. Mesh R-CNN uses both representations to get better quality 3D structure predictions.
A mesh is the surface of a 3D model represented as polygons, where each polygon can be represented as a triangle. Meshes consist of vertices connected by edges. The edge and vertex connection creates faces that have a commonly triangular shape. This representation is good for faster transformations and rendering:
Figure 10.1: Example of a polygon mesh
Voxels are the 3D analogs of 2D pixels. As each image consists of 2D pixels, it is logical to use the same idea to represent 3D data. Each voxel is a cube, and each object is a group of cubes where some of them are the outer visible parts, and some of them are inside the object. It’s easier to visualize 3D objects with voxels, but it’s not the only use case. In deep learning problems, voxels...
