diff --git a/tests/unit/algo/object_detection_3d/matchers/test_matcher_3d.py b/tests/unit/algo/object_detection_3d/matchers/test_matcher_3d.py
deleted file mode 100644
index c2173bd411a..00000000000
--- a/tests/unit/algo/object_detection_3d/matchers/test_matcher_3d.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# Copyright (C) 2024 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-#
-"""Test for HungarianMatcher3D module."""
-
-import pytest
-import torch
-from otx.algo.object_detection_3d.matchers.matcher_3d import HungarianMatcher3D
-
-
-class TestHungarianMatcher3D:
-    @pytest.fixture()
-    def matcher(self):
-        return HungarianMatcher3D()
-
-    def test_hungarian_matcher_3d(self, matcher):
-        outputs = {
-            "scores": torch.randn(1, 100, 10),
-            "boxes_3d": torch.randn(1, 100, 6),
-        }
-        targets = [
-            {
-                "labels": torch.tensor([0, 0, 0, 0]),
-                "boxes": torch.tensor(
-                    [
-                        [0.7697, 0.4923, 0.0398, 0.0663],
-                        [0.7371, 0.4857, 0.0339, 0.0620],
-                        [0.7126, 0.4850, 0.0246, 0.0501],
-                        [0.5077, 0.5280, 0.0444, 0.1475],
-                    ],
-                ),
-                "boxes_3d": torch.tensor(
-                    [
-                        [0.7689, 0.4918, 0.0191, 0.0208, 0.0327, 0.0336],
-                        [0.7365, 0.4858, 0.0163, 0.0175, 0.0310, 0.0310],
-                        [0.7122, 0.4848, 0.0118, 0.0127, 0.0248, 0.0252],
-                        [0.5089, 0.5234, 0.0235, 0.0209, 0.0693, 0.0783],
-                    ],
-                ),
-            },
-        ]
-        group_num = 11
-
-        result = matcher(outputs, targets, group_num)
-
-        assert len(result) == 1
-        assert isinstance(result[0][0], torch.Tensor)
-        assert isinstance(result[0][1], torch.Tensor)
-        assert len(result[0][0].tolist()) == 44
-        assert len(result[0][1].tolist()) == 44
-        assert torch.max(torch.stack(result[0])) <= 100