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# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
import torch
from ...modules.sparse import SparseTensor
from easydict import EasyDict as edict
from .utils_cube import *
from .flexicube import FlexiCubes
class MeshExtractResult:
def __init__(self,
vertices,
faces,
vertex_attrs=None,
res=64
):
self.vertices = vertices
self.faces = faces.long()
self.vertex_attrs = vertex_attrs
self.face_normal = self.comput_face_normals(vertices, faces)
self.res = res
self.success = (vertices.shape[0] != 0 and faces.shape[0] != 0)
# training only
self.tsdf_v = None
self.tsdf_s = None
self.reg_loss = None
def comput_face_normals(self, verts, faces):
i0 = faces[..., 0].long()
i1 = faces[..., 1].long()
i2 = faces[..., 2].long()
v0 = verts[i0, :]
v1 = verts[i1, :]
v2 = verts[i2, :]
face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1)
face_normals = torch.nn.functional.normalize(face_normals, dim=1)
# print(face_normals.min(), face_normals.max(), face_normals.shape)
return face_normals[:, None, :].repeat(1, 3, 1)
def comput_v_normals(self, verts, faces):
i0 = faces[..., 0].long()
i1 = faces[..., 1].long()
i2 = faces[..., 2].long()
v0 = verts[i0, :]
v1 = verts[i1, :]
v2 = verts[i2, :]
face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1)
v_normals = torch.zeros_like(verts)
v_normals.scatter_add_(0, i0[..., None].repeat(1, 3), face_normals)
v_normals.scatter_add_(0, i1[..., None].repeat(1, 3), face_normals)
v_normals.scatter_add_(0, i2[..., None].repeat(1, 3), face_normals)
v_normals = torch.nn.functional.normalize(v_normals, dim=1)
return v_normals
class SparseFeatures2Mesh:
def __init__(self, device="cuda", res=64, use_color=True):
'''
a model to generate a mesh from sparse features structures using flexicube
'''
super().__init__()
self.device=device
self.res = res
self.mesh_extractor = FlexiCubes(device=device)
self.sdf_bias = -1.0 / res
verts, cube = construct_dense_grid(self.res, self.device)
self.reg_c = cube.to(self.device)
self.reg_v = verts.to(self.device)
self.use_color = use_color
self._calc_layout()
def _calc_layout(self):
LAYOUTS = {
'sdf': {'shape': (8, 1), 'size': 8},
'deform': {'shape': (8, 3), 'size': 8 * 3},
'weights': {'shape': (21,), 'size': 21}
}
if self.use_color:
'''
6 channel color including normal map
'''
LAYOUTS['color'] = {'shape': (8, 6,), 'size': 8 * 6}
self.layouts = edict(LAYOUTS)
start = 0
for k, v in self.layouts.items():
v['range'] = (start, start + v['size'])
start += v['size']
self.feats_channels = start
def get_layout(self, feats : torch.Tensor, name : str):
if name not in self.layouts:
return None
return feats[:, self.layouts[name]['range'][0]:self.layouts[name]['range'][1]].reshape(-1, *self.layouts[name]['shape'])
def __call__(self, cubefeats : SparseTensor, training=False):
"""
Generates a mesh based on the specified sparse voxel structures.
Args:
cube_attrs [Nx21] : Sparse Tensor attrs about cube weights
verts_attrs [Nx10] : [0:1] SDF [1:4] deform [4:7] color [7:10] normal
Returns:
return the success tag and ni you loss,
"""
# add sdf bias to verts_attrs
coords = cubefeats.coords[:, 1:]
feats = cubefeats.feats
sdf, deform, color, weights = [self.get_layout(feats, name) for name in ['sdf', 'deform', 'color', 'weights']]
sdf += self.sdf_bias
v_attrs = [sdf, deform, color] if self.use_color else [sdf, deform]
v_pos, v_attrs, reg_loss = sparse_cube2verts(coords, torch.cat(v_attrs, dim=-1), training=training)
v_attrs_d = get_dense_attrs(v_pos, v_attrs, res=self.res+1, sdf_init=True)
weights_d = get_dense_attrs(coords, weights, res=self.res, sdf_init=False)
if self.use_color:
sdf_d, deform_d, colors_d = v_attrs_d[..., 0], v_attrs_d[..., 1:4], v_attrs_d[..., 4:]
else:
sdf_d, deform_d = v_attrs_d[..., 0], v_attrs_d[..., 1:4]
colors_d = None
x_nx3 = get_defomed_verts(self.reg_v, deform_d, self.res)
vertices, faces, L_dev, colors = self.mesh_extractor(
voxelgrid_vertices=x_nx3,
scalar_field=sdf_d,
cube_idx=self.reg_c,
resolution=self.res,
beta=weights_d[:, :12],
alpha=weights_d[:, 12:20],
gamma_f=weights_d[:, 20],
voxelgrid_colors=colors_d,
training=training)
mesh = MeshExtractResult(vertices=vertices, faces=faces, vertex_attrs=colors, res=self.res)
if training:
if mesh.success:
reg_loss += L_dev.mean() * 0.5
reg_loss += (weights[:,:20]).abs().mean() * 0.2
mesh.reg_loss = reg_loss
mesh.tsdf_v = get_defomed_verts(v_pos, v_attrs[:, 1:4], self.res)
mesh.tsdf_s = v_attrs[:, 0]
return mesh
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