geom-shall-be-mesh#
Code |
VG.MESH.001 |
---|---|
Validator |
|
Compatibility |
core usd |
Tags |
🔑 |
Version |
0.1.0 |
Summary#
All geometry shall be represented as mesh primitives using the UsdGeomMesh schema.
Description#
Geometric surfaces shall be encoded as meshes to ensure compatibility with standard rendering pipelines, collision detection systems, and simulation frameworks. Mesh geometry provides surface topology, vertex positions, and face connectivity that can be efficiently processed by rendering hardware and software algorithms.
Curves, point clouds, implicit surfaces, procedural geometry, and other non-mesh representations are not supported in the current specification.
Examples#
#usda 1.0
# Valid: Standard mesh geometry
def Mesh "ValidGeometry" {
point3f[] points = [(0,0,0), (1,0,0), (1,1,0), (0,1,0)]
int[] faceVertexCounts = [4]
int[] faceVertexIndices = [0, 1, 2, 3]
normal3f[] normals = [(0, 0, 1)]
token normals:interpolation = "uniform"
}
# Valid: Triangulated mesh
def Mesh "TriangulatedMesh" {
point3f[] points = [(0,0,0), (1,0,0), (1,1,0), (0,1,0)]
int[] faceVertexCounts = [3, 3]
int[] faceVertexIndices = [0, 1, 2, 0, 2, 3]
normal3f[] normals = [(0, 0, 1), (0, 0, 1)]
token normals:interpolation = "uniform"
}
# Invalid: Point cloud (not supported)
def Points "InvalidPointCloud" {
point3f[] points = [(0,0,0), (1,0,0), (1,1,0), (0,1,0)]
}
# Invalid: Implicit surface (not supported)
def Sphere "InvalidImplicit" {
double radius = 1.0
}
How to comply#
Convert all geometry to mesh representation before export
Ensure proper triangulation or polygonization of curved surfaces
Maintain appropriate mesh density for intended use case
Validate mesh topology and connectivity
Use appropriate interpolation methods for normals and other attributes
Test mesh compatibility with target visualization systems
For More Information#
Notes#
Future Technology Considerations:
This requirement is subject to change as new geometry representation technologies emerge and mature. The following technologies may be considered for future adoption:
Point Clouds: High-density point cloud representations may be supported for large-scale environmental data, LiDAR scans, or photogrammetry workflows
3D Gaussian Splatting (3DGS): Neural rendering techniques using 3D Gaussian representations may be adopted for real-time visualization of complex scenes
Neural Radiance Fields (NeRF): Implicit neural representations may be supported for novel view synthesis and scene reconstruction
Signed Distance Functions (SDF): Implicit surface representations may be adopted for procedural geometry and level-of-detail systems
Voxel Representations: Volumetric data structures may be supported for medical imaging, scientific visualization, or simulation data
Any future adoption of these technologies will be evaluated based on:
Industry standardization and adoption
Performance characteristics and scalability
Integration complexity with existing pipelines
Backward compatibility requirements
Use case specific benefits and trade-offs
The current mesh-based requirement ensures maximum compatibility and reliability while the industry continues to evolve toward more advanced geometry representations.