diff --git a/mmcv/ops/box_iou_rotated.py b/mmcv/ops/box_iou_rotated.py
index a811531d42..8e199d9ac8 100644
--- a/mmcv/ops/box_iou_rotated.py
+++ b/mmcv/ops/box_iou_rotated.py
@@ -142,11 +142,6 @@ def box_iou_rotated(bboxes1: torch.Tensor,
         flip_mat[-1] = -1
         bboxes1 = bboxes1 * flip_mat
         bboxes2 = bboxes2 * flip_mat
-    if bboxes1.device.type == 'npu':
-        scale_mat = bboxes1.new_ones(bboxes1.shape[-1])
-        scale_mat[-1] = 1.0 / 0.01745329252
-        bboxes1 = bboxes1 * scale_mat
-        bboxes2 = bboxes2 * scale_mat
     bboxes1 = bboxes1.contiguous()
     bboxes2 = bboxes2.contiguous()
     ext_module.box_iou_rotated(
diff --git a/mmcv/ops/csrc/pytorch/npu/box_iou_rotated_npu.cpp b/mmcv/ops/csrc/pytorch/npu/box_iou_rotated_npu.cpp
index c6e6b66478..d8b0bbaa67 100644
--- a/mmcv/ops/csrc/pytorch/npu/box_iou_rotated_npu.cpp
+++ b/mmcv/ops/csrc/pytorch/npu/box_iou_rotated_npu.cpp
@@ -8,40 +8,15 @@ void box_iou_rotated_impl(const Tensor boxes1, const Tensor boxes2, Tensor ious,
 
 void box_iou_rotated_npu(const Tensor boxes1, const Tensor boxes2, Tensor ious,
                          const int mode_flag, const bool aligned) {
-  at::Tensor boxes = at::ones_like(boxes1);
-  at::Tensor query_boxes = at::ones_like(boxes2);
-  boxes = boxes1.transpose(0, 1).unsqueeze(0);
-  query_boxes = boxes2.transpose(0, 1).unsqueeze(0);
 
-  bool is_trans = false;
-  string modeStr = "iou";
-  if (mode_flag == 1) {
-    modeStr = "iof";
-  }
-  bool is_cross = true;
-  if (aligned) {
-    is_cross = false;
-  }
-  float v_threshold = 0;
-  float e_threshold = 0;
+  TORCH_CHECK(boxes1.size(1) == 5, "boxes1 must be 2D tensor (N, 5)");
+  TORCH_CHECK(boxes1.size(1) == 5, "boxes1 must be 2D tensor (N, 5)");
 
-  OpCommand cmd;
-  cmd.Name("RotatedIou")
-      .Input(boxes)
-      .Input(query_boxes)
-      .Output(ious)
-      .Attr("trans", is_trans)
-      .Attr("mode", modeStr)
-      .Attr("is_cross", is_cross)
-      .Attr("v_threshold", v_threshold)
-      .Attr("e_threshold", e_threshold)
-      .Run();
-
-  if (is_cross) {
-    ious = ious.view({boxes1.size(0), boxes2.size(0)});
-  } else {
-    ious = ious.view({boxes1.size(0), 1});
-  }
+  auto trans = false;
+  auto is_clockwise = false;
+  EXEC_NPU_CMD(aclnnBoxesOverlapBev, boxes1, boxes2, trans, is_clockwise,
+                aligned, mode_flag, ious);
+  return;
 }
 
 REGISTER_NPU_IMPL(box_iou_rotated_impl, box_iou_rotated_npu);
diff --git a/mmcv/ops/csrc/pytorch/npu/boxes_overlap_bev_npu.cpp b/mmcv/ops/csrc/pytorch/npu/boxes_overlap_bev_npu.cpp
new file mode 100644
index 0000000000..6bc6273083
--- /dev/null
+++ b/mmcv/ops/csrc/pytorch/npu/boxes_overlap_bev_npu.cpp
@@ -0,0 +1,25 @@
+#include "pytorch_npu_helper.hpp"
+
+using namespace NPU_NAME_SPACE;
+using namespace std;
+
+void iou3d_boxes_overlap_bev_forward_impl(const int num_a, const Tensor boxes_a,
+                                          const int num_b, const Tensor boxes_b,
+                                          Tensor ans_overlap);
+
+void iou3d_boxes_overlap_bev_forward_npu(const int num_a, const Tensor boxes_a,
+                                         const int num_b, const Tensor boxes_b,
+                                         Tensor ans_overlap) {
+
+    TORCH_CHECK(boxes_a.size(1) == 7, "boxes_a must be 2D tensor (N, 7)");
+    TORCH_CHECK(boxes_b.size(1) == 7, "boxes_b must be 2D tensor (N, 7)");
+
+    auto trans = false;
+    auto is_clockwise = false;
+    auto aligned = false;
+    auto mode_flag = 2;
+    EXEC_NPU_CMD(aclnnBoxesOverlapBev, boxes_a, boxes_b, trans, is_clockwise, aligned, mode_flag, ans_overlap);
+    return;
+}
+
+REGISTER_NPU_IMPL(iou3d_boxes_overlap_bev_forward_impl, iou3d_boxes_overlap_bev_forward_npu);
diff --git a/tests/test_ops/test_iou3d.py b/tests/test_ops/test_iou3d.py
index 27a09eb361..6b2456e8b9 100644
--- a/tests/test_ops/test_iou3d.py
+++ b/tests/test_ops/test_iou3d.py
@@ -11,7 +11,11 @@
     pytest.param(
         'cuda',
         marks=pytest.mark.skipif(
-            not IS_CUDA_AVAILABLE, reason='requires CUDA support'))
+            not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
+    pytest.param(
+        'npu',
+        marks=pytest.mark.skipif(
+            not IS_NPU_AVAILABLE, reason='requires NPU support'))
 ])
 def test_boxes_overlap_bev(device):
     np_boxes1 = np.asarray([[1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 0.0],