shapenet/data/mesh_vox.py [152:187]:
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        device = voxel_coords.device
        voxel_coords = project_verts(voxel_coords, P)

        # In the original coordinate system, the object fits in a unit sphere
        # centered at the origin. Thus after transforming by RT, it will fit
        # in a unit sphere centered at T = RT[:, 3] = (0, 0, RT[2, 3]). We need
        # to figure out what the range of z will be after being further
        # transformed by K. We can work this out explicitly.
        # z0 = RT[2, 3].item()
        # zp, zm = z0 - 0.5, z0 + 0.5
        # k22, k23 = K[2, 2].item(), K[2, 3].item()
        # k32, k33 = K[3, 2].item(), K[3, 3].item()
        # zmin = (zm * k22 + k23) / (zm * k32 + k33)
        # zmax = (zp * k22 + k23) / (zp * k32 + k33)

        # Using the actual zmin and zmax of the model is bad because we need them
        # to perform the inverse transform, which transform voxels back into world
        # space for refinement or evaluation. Instead we use an empirical min and
        # max over the dataset; that way it is consistent for all images.
        zmin = SHAPENET_MIN_ZMIN
        zmax = SHAPENET_MAX_ZMAX

        # Once we know zmin and zmax, we need to adjust the z coordinates so the
        # range [zmin, zmax] instead runs from [-1, 1]
        m = 2.0 / (zmax - zmin)
        b = -2.0 * zmin / (zmax - zmin) - 1
        voxel_coords[:, 2].mul_(m).add_(b)
        voxel_coords[:, 1].mul_(-1)  # Flip y

        # Now voxels are in [-1, 1]^3; map to [0, V-1)^3
        voxel_coords = 0.5 * (V - 1) * (voxel_coords + 1.0)
        voxel_coords = voxel_coords.round().to(torch.int64)
        valid = (0 <= voxel_coords) * (voxel_coords < V)
        valid = valid[:, 0] * valid[:, 1] * valid[:, 2]
        x, y, z = voxel_coords.unbind(dim=1)
        x, y, z = x[valid], y[valid], z[valid]
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tools/preprocess_shapenet.py [285:308]:
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    device = voxel_coords.device
    voxel_coords = project_verts(voxel_coords, P)

    # Using the actual zmin and zmax of the model is bad because we need them
    # to perform the inverse transform, which transform voxels back into world
    # space for refinement or evaluation. Instead we use an empirical min and
    # max over the dataset; that way it is consistent for all images.
    zmin = SHAPENET_MIN_ZMIN
    zmax = SHAPENET_MAX_ZMAX

    # Once we know zmin and zmax, we need to adjust the z coordinates so the
    # range [zmin, zmax] instead runs from [-1, 1]
    m = 2.0 / (zmax - zmin)
    b = -2.0 * zmin / (zmax - zmin) - 1
    voxel_coords[:, 2].mul_(m).add_(b)
    voxel_coords[:, 1].mul_(-1)  # Flip y

    # Now voxels are in [-1, 1]^3; map to [0, V-1)^3
    voxel_coords = 0.5 * (V - 1) * (voxel_coords + 1.0)
    voxel_coords = voxel_coords.round().to(torch.int64)
    valid = (0 <= voxel_coords) * (voxel_coords < V)
    valid = valid[:, 0] * valid[:, 1] * valid[:, 2]
    x, y, z = voxel_coords.unbind(dim=1)
    x, y, z = x[valid], y[valid], z[valid]
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