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,