diff --git a/cpp/open3d/core/kernel/ReductionCPU.cpp b/cpp/open3d/core/kernel/ReductionCPU.cpp
index 7caa60b34f7..710f1aaea24 100644
--- a/cpp/open3d/core/kernel/ReductionCPU.cpp
+++ b/cpp/open3d/core/kernel/ReductionCPU.cpp
@@ -122,13 +122,14 @@ class CPUReductionEngine {
         for (int64_t thread_idx = 0; thread_idx < num_threads; ++thread_idx) {
             int64_t start = thread_idx * workload_per_thread;
             int64_t end = std::min(start + workload_per_thread, num_workloads);
+            scalar_t local_result = identity;
             for (int64_t workload_idx = start; workload_idx < end;
                  ++workload_idx) {
                 scalar_t* src = reinterpret_cast<scalar_t*>(
                         indexer.GetInputPtr(0, workload_idx));
-                thread_results[thread_idx] =
-                        element_kernel(*src, thread_results[thread_idx]);
+                local_result = element_kernel(*src, local_result);
             }
+            thread_results[thread_idx] = local_result;
         }
         scalar_t* dst = reinterpret_cast<scalar_t*>(indexer.GetOutputPtr(0));
         for (int64_t thread_idx = 0; thread_idx < num_threads; ++thread_idx) {
@@ -190,14 +191,25 @@ class CPUArgReductionEngine {
         // elements. We need to keep track of the indices within each
         // sub-iteration.
         int64_t num_output_elements = indexer_.NumOutputElements();
+        if (num_output_elements <= 1) {
+            LaunchArgReductionKernelTwoPass(indexer_, reduce_func, identity);
+        } else {
+            LaunchArgReductionParallelDim(indexer_, reduce_func, identity);
+        }
+    }
 
+    template <typename scalar_t, typename func_t>
+    static void LaunchArgReductionParallelDim(const Indexer& indexer,
+                                              func_t reduce_func,
+                                              scalar_t identity) {
+        int64_t num_output_elements = indexer.NumOutputElements();
 #pragma omp parallel for schedule(static) \
         num_threads(utility::EstimateMaxThreads())
         for (int64_t output_idx = 0; output_idx < num_output_elements;
              output_idx++) {
             // sub_indexer.NumWorkloads() == ipo.
-            // sub_indexer's workload_idx is indexer_'s ipo_idx.
-            Indexer sub_indexer = indexer_.GetPerOutputIndexer(output_idx);
+            // sub_indexer's workload_idx is indexer's ipo_idx.
+            Indexer sub_indexer = indexer.GetPerOutputIndexer(output_idx);
             scalar_t dst_val = identity;
             for (int64_t workload_idx = 0;
                  workload_idx < sub_indexer.NumWorkloads(); workload_idx++) {
@@ -212,6 +224,52 @@ class CPUArgReductionEngine {
         }
     }
 
+    /// Create num_threads workers to compute partial arg reductions
+    /// and then reduce to the final results.
+    /// This only applies to arg reduction op with one output.
+    template <typename scalar_t, typename func_t>
+    static void LaunchArgReductionKernelTwoPass(const Indexer& indexer,
+                                                func_t reduce_func,
+                                                scalar_t identity) {
+        if (indexer.NumOutputElements() > 1) {
+            utility::LogError(
+                    "Internal error: two-pass arg reduction only works for "
+                    "single-output arg reduction ops.");
+        }
+        int64_t num_workloads = indexer.NumWorkloads();
+        int64_t num_threads = utility::EstimateMaxThreads();
+        int64_t workload_per_thread =
+                (num_workloads + num_threads - 1) / num_threads;
+        std::vector<int64_t> thread_results_idx(num_threads, 0);
+        std::vector<scalar_t> thread_results_val(num_threads, identity);
+
+#pragma omp parallel for schedule(static) \
+        num_threads(utility::EstimateMaxThreads())
+        for (int64_t thread_idx = 0; thread_idx < num_threads; ++thread_idx) {
+            int64_t start = thread_idx * workload_per_thread;
+            int64_t end = std::min(start + workload_per_thread, num_workloads);
+            scalar_t local_result_val = identity;
+            int64_t local_result_idx = 0;
+            for (int64_t workload_idx = start; workload_idx < end;
+                 ++workload_idx) {
+                int64_t src_idx = workload_idx;
+                scalar_t* src_val = reinterpret_cast<scalar_t*>(
+                        indexer.GetInputPtr(0, workload_idx));
+                std::tie(local_result_idx, local_result_val) = reduce_func(
+                        src_idx, *src_val, local_result_idx, local_result_val);
+            }
+            thread_results_val[thread_idx] = local_result_val;
+            thread_results_idx[thread_idx] = local_result_idx;
+        }
+        scalar_t dst_val = identity;
+        int64_t* dst_idx = reinterpret_cast<int64_t*>(indexer.GetOutputPtr(0));
+        for (int64_t thread_idx = 0; thread_idx < num_threads; ++thread_idx) {
+            std::tie(*dst_idx, dst_val) = reduce_func(
+                    thread_results_idx[thread_idx],
+                    thread_results_val[thread_idx], *dst_idx, dst_val);
+        }
+    }
+
 private:
     Indexer indexer_;
 };