voxel_size = (1. / opt.grid_dim) * 1.1 * scale grid_origin = torch.tensor(np.eye(4)).float().to(device).squeeze() grid_origin[:3, 3] = grid_barycenter # Minimum and maximum depth used for rejecting voxels outside of the cmaera frustrum depth_min = 0. depth_max = opt.grid_dim * voxel_size + near_plane grid_dims = 3 * [opt.grid_dim] # Resolution of canonical viewing volume in the depth dimension, in number of voxels. frustrum_depth = 2 * grid_dims[-1] model = DeepVoxels(lifting_img_dims=proj_image_dims, frustrum_img_dims=proj_image_dims, grid_dims=grid_dims, use_occlusion_net=not opt.no_occlusion_net, num_grid_feats=opt.num_grid_feats, nf0=opt.nf0, img_sidelength=input_image_dims[0]) model.to(device) # Projection module projection = ProjectionHelper(projection_intrinsic=proj_intrinsic, lifting_intrinsic=lift_intrinsic, depth_min=depth_min, depth_max=depth_max, projection_image_dims=proj_image_dims, lifting_image_dims=proj_image_dims, grid_dims=grid_dims, voxel_size=voxel_size, device=device,
voxel_size = (1. / opt.grid_dim) * scale grid_origin = torch.tensor(np.eye(4)).float().to(device).squeeze() grid_origin[:3, 3] = grid_barycenter # Minimum and maximum depth used for rejecting voxels outside of the cmaera frustrum depth_min = 0. depth_max = opt.grid_dim * voxel_size + near_plane grid_dims = 3 * [opt.grid_dim] # Resolution of canonical viewing volume in the depth dimension, in number of voxels. frustrum_depth = 2 * grid_dims[-1] model = DeepVoxels(lifting_img_dims=proj_image_dims, frustrum_img_dims=proj_image_dims, grid_dims=grid_dims, use_occlusion_net=not opt.no_occlusion_net, num_grid_feats=opt.num_grid_feats, nf0=opt.nf0, img_sidelength=input_image_dims[0]) model.to(device) # Projection module projection = ProjectionHelper(projection_intrinsic=proj_intrinsic, lifting_intrinsic=lift_intrinsic, depth_min=depth_min, depth_max=depth_max, projection_image_dims=proj_image_dims, lifting_image_dims=proj_image_dims, grid_dims=grid_dims, voxel_size=voxel_size, device=device,