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trellis/representations/mesh/utils_cube.py

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+import torch
+cube_corners = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [
+        1, 0, 1], [0, 1, 1], [1, 1, 1]], dtype=torch.int)
+cube_neighbor = torch.tensor([[1, 0, 0], [-1, 0, 0], [0, 1, 0], [0, -1, 0], [0, 0, 1], [0, 0, -1]])
+cube_edges = torch.tensor([0, 1, 1, 5, 4, 5, 0, 4, 2, 3, 3, 7, 6, 7, 2, 6,
+                2, 0, 3, 1, 7, 5, 6, 4], dtype=torch.long, requires_grad=False)
+     
+def construct_dense_grid(res, device='cuda'):
+    '''construct a dense grid based on resolution'''
+    res_v = res + 1
+    vertsid = torch.arange(res_v ** 3, device=device)
+    coordsid = vertsid.reshape(res_v, res_v, res_v)[:res, :res, :res].flatten()
+    cube_corners_bias = (cube_corners[:, 0] * res_v + cube_corners[:, 1]) * res_v + cube_corners[:, 2]
+    cube_fx8 = (coordsid.unsqueeze(1) + cube_corners_bias.unsqueeze(0).to(device))
+    verts = torch.stack([vertsid // (res_v ** 2), (vertsid // res_v) % res_v, vertsid % res_v], dim=1)
+    return verts, cube_fx8
+
+
+def construct_voxel_grid(coords):
+    verts = (cube_corners.unsqueeze(0).to(coords) + coords.unsqueeze(1)).reshape(-1, 3)
+    verts_unique, inverse_indices = torch.unique(verts, dim=0, return_inverse=True)
+    cubes = inverse_indices.reshape(-1, 8)
+    return verts_unique, cubes
+
+
+def cubes_to_verts(num_verts, cubes, value, reduce='mean'):
+    """
+    Args:
+        cubes [Vx8] verts index for each cube
+        value [Vx8xM] value to be scattered
+    Operation:
+        reduced[cubes[i][j]][k] += value[i][k]
+    """
+    M = value.shape[2] # number of channels
+    reduced = torch.zeros(num_verts, M, device=cubes.device)
+    return torch.scatter_reduce(reduced, 0, 
+        cubes.unsqueeze(-1).expand(-1, -1, M).flatten(0, 1), 
+        value.flatten(0, 1), reduce=reduce, include_self=False)
+    
+def sparse_cube2verts(coords, feats, training=True):
+    new_coords, cubes = construct_voxel_grid(coords)
+    new_feats = cubes_to_verts(new_coords.shape[0], cubes, feats)
+    if training:
+        con_loss = torch.mean((feats - new_feats[cubes]) ** 2)
+    else:
+        con_loss = 0.0
+    return new_coords, new_feats, con_loss
+    
+
+def get_dense_attrs(coords : torch.Tensor, feats : torch.Tensor, res : int, sdf_init=True):
+    F = feats.shape[-1]
+    dense_attrs = torch.zeros([res] * 3 + [F], device=feats.device)
+    if sdf_init:
+        dense_attrs[..., 0] = 1 # initial outside sdf value
+    dense_attrs[coords[:, 0], coords[:, 1], coords[:, 2], :] = feats
+    return dense_attrs.reshape(-1, F)
+
+
+def get_defomed_verts(v_pos : torch.Tensor, deform : torch.Tensor, res):
+    return v_pos / res - 0.5 + (1 - 1e-8) / (res * 2) * torch.tanh(deform)
+