diff --git a/cpp/pybind/geometry/pointcloud.cpp b/cpp/pybind/geometry/pointcloud.cpp
index 2dbb4c741d3..566cdd6b50d 100644
--- a/cpp/pybind/geometry/pointcloud.cpp
+++ b/cpp/pybind/geometry/pointcloud.cpp
@@ -150,7 +150,7 @@ void pybind_pointcloud_definitions(py::module &m) {
                  &PointCloud::OrientNormalsConsistentTangentPlane,
                  "Function to orient the normals with respect to consistent "
                  "tangent planes",
-                 "k"_a, "lambda"_a = 0.0, "cos_alpha_tol"_a = 1.0)
+                 "k"_a, "lambda_penalty"_a = 0.0, "cos_alpha_tol"_a = 1.0)
             .def("compute_point_cloud_distance",
                  &PointCloud::ComputePointCloudDistance,
                  "For each point in the source point cloud, compute the "
diff --git a/cpp/pybind/t/geometry/pointcloud.cpp b/cpp/pybind/t/geometry/pointcloud.cpp
index e5bac211b90..172f92c2356 100644
--- a/cpp/pybind/t/geometry/pointcloud.cpp
+++ b/cpp/pybind/t/geometry/pointcloud.cpp
@@ -331,17 +331,17 @@ infinite value. It also removes the corresponding attributes.
     pointcloud.def(
             "orient_normals_consistent_tangent_plane",
             &PointCloud::OrientNormalsConsistentTangentPlane, "k"_a,
-            "lambda"_a = 0.0, "cos_alpha_tol"_a = 1.0,
+            "lambda_penalty"_a = 0.0, "cos_alpha_tol"_a = 1.0,
             R"(Function to consistently orient the normals of a point cloud based on tangent planes.
 
 The algorithm is described in Hoppe et al., "Surface Reconstruction from Unorganized Points", 1992.
-Additional information about the choice of lambda and cos_alpha_tol for complex
+Additional information about the choice of lambda_penalty and cos_alpha_tol for complex
 point clouds can be found in Piazza, Valentini, Varetti, "Mesh Reconstruction from Point Cloud", 2023
 (https://eugeniovaretti.github.io/meshreco/Piazza_Valentini_Varetti_MeshReconstructionFromPointCloud_2023.pdf).
 
 Args:
     k (int): Number of neighbors to use for tangent plane estimation.
-    lambda (float): A non-negative real parameter that influences the distance
+    lambda_penalty (float): A non-negative real parameter that influences the distance
         metric used to identify the true neighbors of a point in complex
         geometries. It penalizes the distance between a point and the tangent
         plane defined by the reference point and its normal vector, helping to
@@ -354,7 +354,7 @@ point clouds can be found in Piazza, Valentini, Varetti, "Mesh Reconstruction fr
 Example:
     We use Bunny point cloud to compute its normals and orient them consistently.
     The initial reconstruction adheres to Hoppe's algorithm (raw), whereas the
-    second reconstruction utilises the lambda and cos_alpha_tol parameters.
+    second reconstruction utilises the lambda_penalty and cos_alpha_tol parameters.
     Due to the high density of the Bunny point cloud available in Open3D a larger
     value of the parameter k is employed to test the algorithm.  Usually you do
     not have at disposal such a refined point clouds, thus you cannot find a
@@ -379,7 +379,7 @@ point clouds can be found in Piazza, Valentini, Varetti, "Mesh Reconstruction fr
         poisson_mesh.compute_vertex_normals()
         o3d.visualization.draw_geometries([poisson_mesh])
 
-        # Case 2, reconstruction using lambda and cos_alpha_tol parameters:
+        # Case 2, reconstruction using lambda_penalty and cos_alpha_tol parameters:
         pcd_robust = o3d.io.read_point_cloud(data.path)
 
         # Compute normals and orient them consistently, using k=100 neighbours