TorchSDF

This is a custom version of the signed distance field(SDF) computation from the Kaolin library. It supports SDF computation for manifold meshes with PyTorch on GPU.

Purpose

Why don't I use the original Kaolin API?

• I just want to compute SDF. Kaolin is too large and redundant. I want a lighter package.
• With Kaolin, I should use kaolin.metrics.trianglemesh.point_to_mesh_distance and kaolin.ops.mesh.check_sign for SDF computation. But in a simple but not so precise definition, I can get the value in a single cuda kernel function. This is a potential acceleration.
• I can learn knowledge about the cpp interface of PyTorch.

Installation

Require PyTorch installed.

bash install.sh

Usage

The code provides two functions:

• compute_sdf(pointclouds, face_vertices)
  • input
    • unbatched points with shape (num_point , 3)
    • unbatched face_vertices with shape (num_face , 3, 3)
  • returns
    • squared distance
    • normals defined by gradient
    • distance signs (inside -1 and outside 1)
    • closest points
• index_vertices_by_faces(vertices_features, faces): return face_verts reqired by compute_sdf(pointclouds, face_vertices).

Note

• The sign is defined by sign((p - closest_point).dot(face_normal)), check your mesh has perfect normal information.
  • This definition sometimes causes wrong results. For example, there is an acute angle between two faces.
  • So it is not so precise so far.
• Returned normal is defined by (p - closest_point).normalized() or equally $\frac{\partial d}{\partial p}$, not face normal.
• The code only runs on cuda.
• Scripts in tests cannot run independently (require Kaolin API).