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| # Copyright (c) 2022, 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 util.flexicubes import FlexiCubes # replace later | |
| # from dmtet import sdf_reg_loss_batch | |
| import torch.nn.functional as F | |
| def get_center_boundary_index(grid_res, device): | |
| v = torch.zeros((grid_res + 1, grid_res + 1, grid_res + 1), dtype=torch.bool, device=device) | |
| v[grid_res // 2 + 1, grid_res // 2 + 1, grid_res // 2 + 1] = True | |
| center_indices = torch.nonzero(v.reshape(-1)) | |
| v[grid_res // 2 + 1, grid_res // 2 + 1, grid_res // 2 + 1] = False | |
| v[:2, ...] = True | |
| v[-2:, ...] = True | |
| v[:, :2, ...] = True | |
| v[:, -2:, ...] = True | |
| v[:, :, :2] = True | |
| v[:, :, -2:] = True | |
| boundary_indices = torch.nonzero(v.reshape(-1)) | |
| return center_indices, boundary_indices | |
| ############################################################################### | |
| # Geometry interface | |
| ############################################################################### | |
| class FlexiCubesGeometry(object): | |
| def __init__( | |
| self, grid_res=64, scale=2.0, device='cuda', renderer=None, | |
| render_type='neural_render', args=None): | |
| super(FlexiCubesGeometry, self).__init__() | |
| self.grid_res = grid_res | |
| self.device = device | |
| self.args = args | |
| self.fc = FlexiCubes(device, weight_scale=0.5) | |
| self.verts, self.indices = self.fc.construct_voxel_grid(grid_res) | |
| if isinstance(scale, list): | |
| self.verts[:, 0] = self.verts[:, 0] * scale[0] | |
| self.verts[:, 1] = self.verts[:, 1] * scale[1] | |
| self.verts[:, 2] = self.verts[:, 2] * scale[1] | |
| else: | |
| self.verts = self.verts * scale | |
| all_edges = self.indices[:, self.fc.cube_edges].reshape(-1, 2) | |
| self.all_edges = torch.unique(all_edges, dim=0) | |
| # Parameters used for fix boundary sdf | |
| self.center_indices, self.boundary_indices = get_center_boundary_index(self.grid_res, device) | |
| self.renderer = renderer | |
| self.render_type = render_type | |
| def getAABB(self): | |
| return torch.min(self.verts, dim=0).values, torch.max(self.verts, dim=0).values | |
| def get_mesh(self, v_deformed_nx3, sdf_n, weight_n=None, with_uv=False, indices=None, is_training=False): | |
| if indices is None: | |
| indices = self.indices | |
| verts, faces, v_reg_loss = self.fc(v_deformed_nx3, sdf_n, indices, self.grid_res, | |
| beta_fx12=weight_n[:, :12], alpha_fx8=weight_n[:, 12:20], | |
| gamma_f=weight_n[:, 20], training=is_training | |
| ) | |
| return verts, faces, v_reg_loss | |
| def render_mesh(self, mesh_v_nx3, mesh_f_fx3, camera_mv_bx4x4, resolution=256, hierarchical_mask=False): | |
| return_value = dict() | |
| if self.render_type == 'neural_render': | |
| tex_pos, mask, hard_mask, rast, v_pos_clip, mask_pyramid, depth = self.renderer.render_mesh( | |
| mesh_v_nx3.unsqueeze(dim=0), | |
| mesh_f_fx3.int(), | |
| camera_mv_bx4x4, | |
| mesh_v_nx3.unsqueeze(dim=0), | |
| resolution=resolution, | |
| device=self.device, | |
| hierarchical_mask=hierarchical_mask | |
| ) | |
| return_value['tex_pos'] = tex_pos | |
| return_value['mask'] = mask | |
| return_value['hard_mask'] = hard_mask | |
| return_value['rast'] = rast | |
| return_value['v_pos_clip'] = v_pos_clip | |
| return_value['mask_pyramid'] = mask_pyramid | |
| return_value['depth'] = depth | |
| else: | |
| raise NotImplementedError | |
| return return_value | |
| def render(self, v_deformed_bxnx3=None, sdf_bxn=None, camera_mv_bxnviewx4x4=None, resolution=256): | |
| # Here I assume a batch of meshes (can be different mesh and geometry), for the other shapes, the batch is 1 | |
| v_list = [] | |
| f_list = [] | |
| n_batch = v_deformed_bxnx3.shape[0] | |
| all_render_output = [] | |
| for i_batch in range(n_batch): | |
| verts_nx3, faces_fx3 = self.get_mesh(v_deformed_bxnx3[i_batch], sdf_bxn[i_batch]) | |
| v_list.append(verts_nx3) | |
| f_list.append(faces_fx3) | |
| render_output = self.render_mesh(verts_nx3, faces_fx3, camera_mv_bxnviewx4x4[i_batch], resolution) | |
| all_render_output.append(render_output) | |
| # Concatenate all render output | |
| return_keys = all_render_output[0].keys() | |
| return_value = dict() | |
| for k in return_keys: | |
| value = [v[k] for v in all_render_output] | |
| return_value[k] = value | |
| # We can do concatenation outside of the render | |
| return return_value | |