diff --git a/pytorch3d/ops/mesh_curvature.py b/pytorch3d/ops/mesh_curvature.py
new file mode 100644
index 000000000..99285d07d
--- /dev/null
+++ b/pytorch3d/ops/mesh_curvature.py
@@ -0,0 +1,106 @@
+import torch
+import pytorch3d 
+# from pytorch3d.ops import cot_laplacian
+# from pytorch3d.structures import Meshes
+
+def one_hot_sparse(A,num_classes,value=None):
+    A = A.int()
+    B = torch.arange(A.shape[0]).to(A.device)
+    if value==None:
+        C = torch.ones_like(B)
+    else:
+        C = value
+    return torch.sparse_coo_tensor(torch.stack([B,A]),C,size=(A.shape[0],num_classes))
+
+def faces_angle(meshs: pytorch3d.structures.Meshes)->torch.Tensor:
+    """
+    Compute the angle of each face in a mesh
+    Args:
+        meshs: Meshes object
+    Returns:
+        angles: Tensor of shape (N,3) where N is the number of faces
+    """
+    Face_coord = meshs.verts_packed()[meshs.faces_packed()]
+    A = Face_coord[:,1,:] - Face_coord[:,0,:]
+    B = Face_coord[:,2,:] - Face_coord[:,1,:]
+    C = Face_coord[:,0,:] - Face_coord[:,2,:]
+    angle_0 = torch.arccos(-torch.sum(A*C,dim=1)/torch.norm(A,dim=1)/torch.norm(C,dim=1))
+    angle_1 = torch.arccos(-torch.sum(A*B,dim=1)/torch.norm(A,dim=1)/torch.norm(B,dim=1))
+    angle_2 = torch.arccos(-torch.sum(B*C,dim=1)/torch.norm(B,dim=1)/torch.norm(C,dim=1))
+    angles = torch.stack([angle_0,angle_1,angle_2],dim=1)
+    return angles
+
+def dual_area_weights_on_faces(Surfaces: pytorch3d.structures.Meshes)->torch.Tensor:
+    """
+    Compute the dual area weights of 3 vertices of each triangles in a mesh
+    Args:
+        Surfaces: Meshes object
+    Returns:
+        dual_area_weight: Tensor of shape (N,3) where N is the number of triangles
+        the dual area of a vertices in a triangles is defined as the area of the sub-quadrilateral divided by three perpendicular bisectors
+    """
+    angles = faces_angle(Surfaces)
+    sin2angle = torch.sin(2*angles)
+    dual_area_weight = torch.ones_like(Surfaces.faces_packed())*(torch.sum(sin2angle,dim=1).view(-1,1).repeat(1,3))
+    for i in range(3):
+        j,k = (i+1)%3, (i+2)%3
+        dual_area_weight[:,i] = 0.5*(sin2angle[:,j]+sin2angle[:,k])/dual_area_weight[:,i]
+    return dual_area_weight
+
+
+def Dual_area_for_vertices(Surfaces: pytorch3d.structures.Meshes)->torch.Tensor:
+    """
+    Compute the dual area of each vertices in a mesh
+    Args:
+        Surfaces: Meshes object
+    Returns:
+        dual_area_vertex: Tensor of shape (N,1) where N is the number of vertices
+        the dual area of a vertices is defined as the sum of the dual area of the triangles that contains this vertices
+    """
+
+    dual_area_weight = dual_area_weights_on_faces(Surfaces)
+    dual_area_faces = Surfaces.faces_areas_packed().view(-1,1).repeat(1,3)*dual_area_weight
+    face_vertices_to_idx = one_hot_sparse(Surfaces.faces_packed().view(-1),num_classes=Surfaces.num_verts_per_mesh().sum())
+    dual_area_vertex = torch.sparse.mm(face_vertices_to_idx.float().T,dual_area_faces.view(-1,1)).T
+    return dual_area_vertex
+
+
+def Gaussian_curvature(Surfaces: pytorch3d.structures.Meshes,return_topology=False)->torch.Tensor:
+    """
+    Compute the gaussian curvature of each vertices in a mesh by local Gauss-Bonnet theorem
+    Args:
+        Surfaces: Meshes object
+        return_topology: bool, if True, return the Euler characteristic and genus of the mesh
+    Returns:
+        gaussian_curvature: Tensor of shape (N,1) where N is the number of vertices
+        the gaussian curvature of a vertices is defined as the sum of the angles of the triangles that contains this vertices minus 2*pi and divided by the dual area of this vertices
+    """
+
+    face_vertices_to_idx = one_hot_sparse(Surfaces.faces_packed().view(-1),num_classes=Surfaces.num_verts_per_mesh().sum())
+    vertices_to_meshid = one_hot_sparse(Surfaces.verts_packed_to_mesh_idx(),num_classes=Surfaces.num_verts_per_mesh().shape[0])
+    sum_angle_for_vertices = torch.sparse.mm(face_vertices_to_idx.float().T,faces_angle(Surfaces).view(-1,1)).T
+    # Euler_chara = torch.sparse.mm(vertices_to_meshid.float().T,(2*torch.pi - sum_angle_for_vertices).T).T/torch.pi/2
+    # Euler_chara = torch.round(Euler_chara)
+    # print('Euler_characteristic:',Euler_chara)
+    # Genus = (2-Euler_chara)/2
+    #print('Genus:',Genus)
+    gaussian_curvature = (2*torch.pi - sum_angle_for_vertices)/Dual_area_for_vertices(Surfaces)
+    if return_topology:
+        Euler_chara = torch.sparse.mm(vertices_to_meshid.float().T,(2*torch.pi - sum_angle_for_vertices).T).T/torch.pi/2
+        Euler_chara = torch.round(Euler_chara)
+        return gaussian_curvature, Euler_chara, Genus
+    return gaussian_curvature
+
+def Average_from_verts_to_face(Surfaces: pytorch3d.structures.Meshes, vect_verts: torch.Tensor)->torch.Tensor:
+    """
+    Compute the average of feature vectors defined on vertices to faces by dual area weights
+    Args:
+        Surfaces: Meshes object
+        vect_verts: Tensor of shape (N,C) where N is the number of vertices, C is the number of feature channels
+    Returns:
+        vect_faces: Tensor of shape (F,C) where F is the number of faces
+    """
+    assert vect_verts.shape[0] == Surfaces.verts_packed().shape[0]
+    dual_weight = dual_area_weights_on_faces(Surfaces).view(-1)
+    wg = one_hot_sparse(Surfaces.faces_packed().view(-1),num_classes=Surfaces.num_verts_per_mesh().sum(),value=dual_weight).float()
+    return torch.sparse.mm(wg,vect_verts).view(-1,3).sum(dim=1)
diff --git a/pytorch3d/ops/pcl_curvature.py b/pytorch3d/ops/pcl_curvature.py
new file mode 100644
index 000000000..798b849b4
--- /dev/null
+++ b/pytorch3d/ops/pcl_curvature.py
@@ -0,0 +1,113 @@
+import torch
+import pytorch3d 
+from pytorch3d.ops import knn_points,knn_gather
+# from pytorch3d.structures import Meshes
+
+def Weingarten_maps(pointscloud:torch.Tensor, k=50)->torch.Tensor:
+    """
+    Compute the Weingarten maps of a point cloud
+    Args:
+        pointscloud: Tensor of shape (B,N,3) where N is the number of points in each batch
+        k: int, number of neighbors
+    Returns:
+        Weingarten_fields: Tensor of shape (B,N,2,2) where N is the number of points
+        normals_field: Tensor of shape (B,N,3) where N is the number of points
+        tangent1_field: Tensor of shape (B,N,3) 
+        tangent2_field: Tensor of shape (B,N,3) 
+    """
+
+
+    pointscloud_shape = pointscloud.shape
+    batch_size = pointscloud_shape[0]
+    num_points = torch.LongTensor([pointscloud_shape[1]]*batch_size)
+
+    # undo global mean for stability
+    pointscloud_centered = pointscloud - pointscloud.mean(-2).view(batch_size,1,3)
+    knn_info = knn_points(pointscloud_centered,pointscloud_centered,lengths1=num_points,lengths2=num_points,K=k,return_nn=True)
+    
+    # compute knn & covariance matrix 
+    knn_point_centered = knn_info.knn - knn_info.knn.mean(-2).view(batch_size,-1,1,3)
+    covs_field = torch.matmul(knn_point_centered.transpose(-1,-2),knn_point_centered) /(knn_point_centered.shape[-1]-1)
+    frames_field = torch.linalg.eigh(covs_field).eigenvectors
+
+    normals_field = frames_field[:,:,:,0]
+    tangent1_field = frames_field[:,:,:,1]
+    tangent2_field = frames_field[:,:,:,2]
+
+
+    local_pt_difference = knn_info.knn[:,:,1:k,:]- pointscloud_centered[:,:,None,:] # B x N x K x 3
+
+    # Disambiguates normals by checking the sign of the projection of the
+    proj = (normals_field[:, :, None] * local_pt_difference).sum(-1)
+    # check how many projections are positive
+    n_pos = (proj > 0).type_as(knn_info.knn).sum(-1, keepdim=True)
+    # flip the principal directions where number of positive correlations for 
+    flip = (n_pos < (0.5 * (k-1))).type_as(knn_info.knn)
+
+    normals_field = (1.0 - 2.0 * flip) * normals_field
+
+    # local normals difference
+    local_normals_difference = knn_gather(normals_field,knn_info.idx,lengths=num_points)[:,:,1:k,:] - normals_field[:,:,None,:]
+
+    # project the difference onto the tangent plane, getting the differential of the gaussian map
+    local_dpt_tangent1 = (local_pt_difference * tangent1_field[:,:,None,:]).sum(-1,keepdim=True)
+    local_dpt_tangent2 = (local_pt_difference * tangent2_field[:,:,None,:]).sum(-1,keepdim=True)
+    local_dpt_tangent = torch.cat((local_dpt_tangent1,local_dpt_tangent2),dim=-1)
+    local_dnormals_tangent1 = (local_normals_difference * tangent1_field[:,:,None,:]).sum(-1,keepdim=True)
+    local_dnormals_tangent2 = (local_normals_difference * tangent2_field[:,:,None,:]).sum(-1,keepdim=True)
+    local_dnormals_tangent = torch.cat((local_dnormals_tangent1,local_dnormals_tangent2),dim=-1)
+
+
+    # estimate the weingarten map by solving a least squares problem: W = Dn^T Dp (Dp^T Dp)^-1
+    XXT = torch.matmul(local_dpt_tangent.transpose(-1,-2),local_dpt_tangent)
+    YXT = torch.matmul(local_dnormals_tangent.transpose(-1,-2),local_dpt_tangent)
+    XYT = torch.matmul(local_dpt_tangent.transpose(-1,-2),local_dnormals_tangent)
+    #Weingarten_fields_0 = torch.matmul(YXT,torch.inverse(XXT+1e-8*torch.eye(2).type_as(XXT))) ## the unsymetric version
+
+
+    # solve the sylvester equation to get the shape operator (symmetric version)
+    S = YXT + XYT
+
+    XXT_eig = torch.linalg.eigh(XXT)
+    Q = XXT_eig.eigenvectors
+    #D = torch.diag_embed(XXT_eig.eigenvalues)
+    # XX^T = Q^T D Q
+    Q_TSQ = torch.matmul(Q.transpose(-1,-2),torch.matmul(S,Q))
+
+    a = XXT_eig.eigenvalues[:,:,0]
+    b = XXT_eig.eigenvalues[:,:,1]
+    a_b = a+b
+    a2_a_b = torch.stack((2*a,a_b),dim=-1).view(batch_size,-1,1,2)
+    a_b_b2 = torch.stack((a_b,2*b),dim=-1).view(batch_size,-1,1,2)
+    c = torch.stack((a2_a_b,a_b_b2),dim=-2).view(batch_size,-1,2,2)
+
+    E = (1/c+1e-6) * Q_TSQ
+    Weingarten_fields = torch.matmul(Q,torch.matmul(E,Q.transpose(-1,-2)))
+
+
+    return Weingarten_fields, normals_field, tangent1_field, tangent2_field
+
+def Curvature_pcl(pointscloud, k=50, return_princpals=False):
+    """
+    Compute the gaussian curvature of point clouds
+    pointscloud: B x N x 3
+    k: int, number of neighbors
+    return_princpals: bool,if True, return principal curvature and principal directions
+    if False, return gaussian curvature, mean curvature only
+    """
+
+    pointscloud_shape = pointscloud.shape
+    batch_size = pointscloud_shape[0]
+    num_points = torch.LongTensor([pointscloud_shape[1]]*batch_size)
+    Weingarten_fields, normals_field, tangent1_field, tangent2_field = Weingarten_maps(pointscloud, k=k)
+    tangent_space = tangent_space = torch.cat((tangent1_field.view(batch_size,-1,1,3),tangent2_field.view(batch_size,-1,1,3)),dim=-2)
+    if return_princpals:
+        principal_curvature , principal_direction_local = torch.linalg.eigh(Weingarten_fields)
+        principal_direction_global = torch.matmul(principal_direction_local.transpose(-1,-2),tangent_space)
+        return principal_curvature, principal_direction_global, normals_field
+    else:
+        gaussian_curvature_pcl = torch.det(Weingarten_fields)
+        mean_curvature_pcl = Weingarten_fields.diagonal(offset=0, dim1=-1, dim2=-2).mean(-1)
+        return gaussian_curvature_pcl, mean_curvature_pcl
+    
+