diff --git a/pytorch3d/loss/chamfer.py b/pytorch3d/loss/chamfer.py
index 58ac3daa..f37f5938 100644
--- a/pytorch3d/loss/chamfer.py
+++ b/pytorch3d/loss/chamfer.py
@@ -145,11 +145,11 @@ def chamfer_distance(
     cham_norm_x = x.new_zeros(())
     cham_norm_y = x.new_zeros(())
 
-    x_dists, x_idx = knn_points(x, y, lengths1=x_lengths, lengths2=y_lengths, K=1)
-    y_dists, y_idx = knn_points(y, x, lengths1=y_lengths, lengths2=x_lengths, K=1)
+    x_nn = knn_points(x, y, lengths1=x_lengths, lengths2=y_lengths, K=1)
+    y_nn = knn_points(y, x, lengths1=y_lengths, lengths2=x_lengths, K=1)
 
-    cham_x = x_dists[..., 0]  # (N, P1)
-    cham_y = y_dists[..., 0]  # (N, P2)
+    cham_x = x_nn.dists[..., 0]  # (N, P1)
+    cham_y = y_nn.dists[..., 0]  # (N, P2)
 
     if is_x_heterogeneous:
         cham_x[x_mask] = 0.0
@@ -162,8 +162,8 @@ def chamfer_distance(
 
     if return_normals:
         # Gather the normals using the indices and keep only value for k=0
-        x_normals_near = knn_gather(y_normals, x_idx, y_lengths)[..., 0, :]
-        y_normals_near = knn_gather(x_normals, y_idx, x_lengths)[..., 0, :]
+        x_normals_near = knn_gather(y_normals, x_nn.idx, y_lengths)[..., 0, :]
+        y_normals_near = knn_gather(x_normals, y_nn.idx, x_lengths)[..., 0, :]
 
         cham_norm_x = 1 - torch.abs(
             F.cosine_similarity(x_normals, x_normals_near, dim=2, eps=1e-6)