Add voxel_range option for mesh to voxel conversion

[kosuke55]

Hi, thank you for the work.

Some meshes cannot be voxelized unless normalize=True.
The ShapeNet_Voxels class also raises an error with some data.

kaolin/kaolin/datasets/shapenet.py

Line 409 in ec193ff

tfs.TriangleMeshToVoxelGrid(res, normalize=False, vertex_offset=0.5),

For example, this object cannot be voxelized.
http://shapenet.cs.stanford.edu/shapenet/obj-zip/ShapeNetCore.v1/04379243/10657fcfce1d326b30bbd4cddd04c77b/model.obj

import kaolin as kal

mesh = kal.rep.TriangleMesh.from_obj(
    '/media/kosuke/SANDISK/meshdata/ShapeNetCore.v2/04379243/10657fcfce1d326b30bbd4cddd04c77b/model.obj')
print(mesh.vertices.min(), mesh.vertices.max())  # tensor(-0.3166) tensor(0.5718)

The maximum value of this mesh is 0.5718, and when it is moved by the offset, it becomes 1.0718.
Since the range of voxelization is 0 to 1, an error occurs due to overflow.

In [2]: voxels = kal.conversions.trianglemesh_to_voxelgrid(mesh, 32, normalize=False, vertex_offset=0.5)
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-2-dd3bce34a4e9> in <module>
----> 1 voxels = kal.conversions.trianglemesh_to_voxelgrid(mesh, 32, normalize=False, vertex_offset=0.5)

~/.pyenv/versions/anaconda3-5.3.1/envs/kaolin/lib/python3.6/site-packages/kaolin-0.1.0-py3.6-linux-x86_64.egg/kaolin/conversions/meshconversions.py in trianglemesh_to_voxelgrid(mesh, resolution, normalize, vertex_offset, voxel_range)
    140     points = torch.split(points.permute(1, 0), 1, dim=0)
    141     points = [m.unsqueeze(0) for m in points]
--> 142     voxel[points] = 1
    143     return voxel
    144

IndexError: index 32 is out of bounds for dimension 0 with size 32

This PR solved this problem by expanding voxel_range.

In [3]: voxels = kal.conversions.trianglemesh_to_voxelgrid(mesh, 32, normalize=False, vertex_offset=0.5, voxel_range=1.08)