diff --git a/pytorch3d/ops/laplacian_matrices.py b/pytorch3d/ops/laplacian_matrices.py
index 6400923f..a7e9b49a 100644
--- a/pytorch3d/ops/laplacian_matrices.py
+++ b/pytorch3d/ops/laplacian_matrices.py
@@ -48,7 +48,7 @@ def laplacian(verts: torch.Tensor, edges: torch.Tensor) -> torch.Tensor:
     # A[e0, e1] = 1 &  A[e1, e0] = 1
     ones = torch.ones(idx.shape[1], dtype=torch.float32, device=verts.device)
     # pyre-fixme[16]: Module `sparse` has no attribute `FloatTensor`.
-    A = torch.sparse.FloatTensor(idx, ones, (V, V))
+    A = torch.sparse_coo_tensor(idx, ones, (V, V))
 
     # the sum of i-th row of A gives the degree of the i-th vertex
     deg = torch.sparse.sum(A, dim=1).to_dense()
@@ -63,14 +63,14 @@ def laplacian(verts: torch.Tensor, edges: torch.Tensor) -> torch.Tensor:
     deg1 = torch.where(deg1 > 0.0, 1.0 / deg1, deg1)
     val = torch.cat([deg0, deg1])
     # pyre-fixme[16]: Module `sparse` has no attribute `FloatTensor`.
-    L = torch.sparse.FloatTensor(idx, val, (V, V))
+    L = torch.sparse_coo_tensor(idx, val, (V, V))
 
     # Then we add the diagonal values L[i, i] = -1.
     idx = torch.arange(V, device=verts.device)
     idx = torch.stack([idx, idx], dim=0)
     ones = torch.ones(idx.shape[1], dtype=torch.float32, device=verts.device)
     # pyre-fixme[16]: Module `sparse` has no attribute `FloatTensor`.
-    L -= torch.sparse.FloatTensor(idx, ones, (V, V))
+    L -= torch.sparse_coo_tensor(idx, ones, (V, V))
 
     return L
 
@@ -127,7 +127,7 @@ def cot_laplacian(
     jj = faces[:, [2, 0, 1]]
     idx = torch.stack([ii, jj], dim=0).view(2, F * 3)
     # pyre-fixme[16]: Module `sparse` has no attribute `FloatTensor`.
-    L = torch.sparse.FloatTensor(idx, cot.view(-1), (V, V))
+    L = torch.sparse_coo_tensor(idx, cot.view(-1), (V, V))
 
     # Make it symmetric; this means we are also setting
     # L[v2, v1] = cota
@@ -176,7 +176,7 @@ def norm_laplacian(
 
     V = verts.shape[0]
     # pyre-fixme[16]: Module `sparse` has no attribute `FloatTensor`.
-    L = torch.sparse.FloatTensor(e01, w01, (V, V))
+    L = torch.sparse_coo_tensor(e01, w01, (V, V))
     L = L + L.t()
 
     return L