Source code for pytorch3d.ops.mesh_filtering

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

import torch
from pytorch3d.ops import norm_laplacian
from pytorch3d.structures import Meshes, utils as struct_utils


# ------------------------ Mesh Smoothing ------------------------ #
# This file contains differentiable operators to filter meshes
# The ops include
# 1) Taubin Smoothing
# TODO(gkioxari) add more! :)
# ---------------------------------------------------------------- #


# ----------------------- Taubin Smoothing ----------------------- #


[docs]def taubin_smoothing( meshes: Meshes, lambd: float = 0.53, mu: float = -0.53, num_iter: int = 10 ) -> Meshes: """ Taubin smoothing [1] is an iterative smoothing operator for meshes. At each iteration verts := (1 - λ) * verts + λ * L * verts verts := (1 - μ) * verts + μ * L * verts This function returns a new mesh with smoothed vertices. Args: meshes: Meshes input to be smoothed lambd, mu: float parameters for Taubin smoothing, lambd > 0, mu < 0 num_iter: number of iterations to execute smoothing Returns: mesh: Smoothed input Meshes [1] Curve and Surface Smoothing without Shrinkage, Gabriel Taubin, ICCV 1997 """ verts = meshes.verts_packed() # V x 3 edges = meshes.edges_packed() # E x 3 for _ in range(num_iter): L = norm_laplacian(verts, edges) total_weight = torch.sparse.sum(L, dim=1).to_dense().view(-1, 1) verts = (1 - lambd) * verts + lambd * torch.mm(L, verts) / total_weight # pyre-ignore L = norm_laplacian(verts, edges) total_weight = torch.sparse.sum(L, dim=1).to_dense().view(-1, 1) verts = (1 - mu) * verts + mu * torch.mm(L, verts) / total_weight verts_list = struct_utils.packed_to_list( verts, meshes.num_verts_per_mesh().tolist() ) mesh = Meshes(verts=list(verts_list), faces=meshes.faces_list()) return mesh