# cuBVH

A CUDA Mesh BVH acceleration toolkit.

### Install

```bash
pip install git+https://github.com/ashawkey/cubvh

# or locally
git clone --recursive https://github.com/ashawkey/cubvh
cd cubvh
pip install .
```
It will take several minutes to build the CUDA dependency.

#### Trouble Shooting
**`fatal error: eigen/matrix.h: No such file or directory`**

This is a known issue for `torch==2.1.0` and `torch==2.1.1` (https://github.com/pytorch/pytorch/issues/112841). 
To patch up these two versions, clone this repository, and copy `patch/eigen` to your pytorch include directory:
```bash
# for example, if you are using anaconda (assume base env)
cp -r patch/eigen ~/anaconda3/lib/python3.9/site-packages/torch/include/pybind11/
```

**`fatal error: Eigen/Dense: No such file or directory`**

Please make sure [`eigen >= 3.3`](https://eigen.tuxfamily.org/index.php?title=Main_Page) is installed. 
We have included it as a submodule in this repository, but you can also install it in your system include path.
(For example, ubuntu systems can use `sudo apt install libeigen3-dev`.)

### Usage

**Basics:**

```python
import numpy as np
import trimesh
import torch

import cubvh

### build BVH from mesh
mesh = trimesh.load('example.ply')
# NOTE: you need to normalize the mesh first, since the max distance is hard-coded to 10.
BVH = cubvh.cuBVH(mesh.vertices, mesh.faces) # build with numpy.ndarray/torch.Tensor

### query ray-mesh intersection
rays_o, rays_d = get_ray(pose, intrinsics, H, W) # [N, 3], [N, 3], query with torch.Tensor (cuda)
intersections, face_id, depth = BVH.ray_trace(rays_o, rays_d) # [N, 3], [N,], [N,]

### query unsigned distance
points # [N, 3]
# uvw is the barycentric corrdinates of the closest point on the closest face (None if `return_uvw` is False).
distances, face_id, uvw = BVH.unsigned_distance(points, return_uvw=True) # [N], [N], [N, 3]

### query signed distance (INNER is NEGATIVE!)
# for watertight meshes (default)
distances, face_id, uvw = BVH.signed_distance(points, return_uvw=True, mode='watertight') # [N], [N], [N, 3]
# for non-watertight meshes:
distances, face_id, uvw = BVH.signed_distance(points, return_uvw=True, mode='raystab') # [N], [N], [N, 3]
```

**Robust Mesh Occupancy:**

UDF + flood-fill for possibly non-watertight/single-layer meshes:

```python
import torch
import cubvh
import numpy as np
from skimage import morphology

resolution = 512
device = torch.device('cuda')

BVH = cubvh.cuBVH(vertices, faces)

grid_points = torch.stack(
    torch.meshgrid(
        torch.linspace(-1, 1, resolution, device=device),
        torch.linspace(-1, 1, resolution, device=device),
        torch.linspace(-1, 1, resolution, device=device),
        indexing="ij",
    ), dim=-1,
) # [N, N, N, 3]

udf, _, _ = BVH.unsigned_distance(grid_points.view(-1, 3), return_uvw=False)
udf = udf.cpu().numpy().reshape(resolution, resolution, resolution)
occ = udf < 2 / resolution # tolerance 2 voxels

empty_mask = morphology.flood(occ, (0, 0, 0), connectivity=1) # flood from the corner, which is for sure empty
occ = ~empty_mask
```
Check [`test/extract_mesh_watertight.py`](test/extract_mesh_watertight.py) for more details.


**Renderer:**

Example for a mesh normal renderer by `ray_trace`:

```bash
python test/renderer.py # default, show a dodecahedron
python test/renderer.py --mesh example.ply # show any mesh file
```

https://user-images.githubusercontent.com/25863658/183238748-7ac82808-6cd3-4bb6-867a-9c22f8e3f7dd.mp4


### Acknowledgement

* Credits to [Thomas Müller](https://tom94.net/)'s amazing [tiny-cuda-nn](https://github.com/NVlabs/tiny-cuda-nn) and [instant-ngp](https://github.com/NVlabs/instant-ngp)!