cumesh2sdf

A mesh to SDF algorithm implemented with CUDA.

PyTorch Plugin

Installation

You will need a working version of PyTorch and CUDA development toolkit.

pip install git+https://github.com/eliphatfs/cumesh2sdf.git

Import the library as:

import torch
import torchcumesh2sdf

API

`get_sdf`

torchcumesh2sdf.get_sdf(
    tris: torch.Tensor,
    R: int, band: float,
    B: int = 16384
) -> torch.Tensor

Parameters:

tris: input triangle soup of shape (F, 3, 3) with dtype float32.
R: resolution of output SDF volume. Required to be power of 2.
band: size of the band near the mesh to compute values. In regions farther than the band, only the sign with a large value 1e9 will be set. Recommended values: 3/R or 4/R.
B: batch size of triangles fed at once to the algorithm. This has no effect on the results (but can affect performance), unless the algorithm meets overflow of int32 indices. It is recommended to leave as default unless you see a warning message.

Returns: (R, R, R) SDF volume with dtype float32.

`get_udf`

torchcumesh2sdf.get_udf(
    tris: torch.Tensor,
    R: int, band: float,
    B: int = 16384
) -> torch.Tensor

Parameters are the same as get_sdf, but this function instructs the algorithm to generate an unsigned distance field volume. This is faster than get_sdf.

`free_cached_memory`

torchcumesh2sdf.free_cached_memory()

torchcumesh2sdf caches allocations to make computation faster. You may call this method to free any cached memory allocations.

Example

See example.py for an example script.

Standalone

Clone and compile with:

bash build.sh

eliphatfs / cumesh2sdf