diff --git a/tutorial/tutorial_visualization.ipynb b/tutorial/tutorial_visualization.ipynb
index 47ce79d..95eac56 100644
--- a/tutorial/tutorial_visualization.ipynb
+++ b/tutorial/tutorial_visualization.ipynb
@@ -190,6 +190,103 @@
         "plt.show()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize trajectories in the camera frame\n",
+        "Script to visualize the traj in te camera frame, supports the cases when the trajectory overlaps over 2 cameras e.g. front and left."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# cast to the camera \n",
+        "def get_pixels(scene, agent, cam='front',human=False):\n",
+        "    frame_idx = scene.scene_metadata.num_history_frames - 1\n",
+        "    if cam == 'front':\n",
+        "        camera = scene.frames[frame_idx].cameras.cam_f0\n",
+        "    elif cam == 'left':\n",
+        "        camera = scene.frames[frame_idx].cameras.cam_l0\n",
+        "    else:\n",
+        "        camera = scene.frames[frame_idx].cameras.cam_r0\n",
+        "    if human:\n",
+        "        agent_trajectory = scene.get_future_trajectory()\n",
+        "    else:\n",
+        "        agent_trajectory = agent.compute_trajectory(scene.get_agent_input())\n",
+        "    trajectory = agent_trajectory.poses\n",
+        "    # transformation matrices \n",
+        "    R_c2l = camera.sensor2lidar_rotation        # camera-to-lidar (3x3)\n",
+        "    T_c2l = camera.sensor2lidar_translation     # camera-to-lidar (3,)\n",
+        "    intrinsics = camera.intrinsics\n",
+        "    # add z \n",
+        "    trajectory_xyz = np.hstack([trajectory[:, :2], np.zeros((trajectory.shape[0], 1))])  # (N, 3)\n",
+        "    \n",
+        "    # lidar_to_camera = inverse of camera_to_lidar\n",
+        "    R_l2c = R_c2l.T                              # inverse of rotation\n",
+        "    T_l2c = -R_l2c @ T_c2l                       # inverse of translation\n",
+        "    \n",
+        "    # apply to points\n",
+        "    trajectory_cam = (R_l2c @ trajectory_xyz.T).T + T_l2c  # shape: (N, 3)\n",
+        "    in_front = trajectory_cam[:, 2] > 0\n",
+        "    points_cam = trajectory_cam[in_front]\n",
+        "    \n",
+        "    # project to image plane using intrinsics\n",
+        "    K = intrinsics  # 3x3\n",
+        "    projected = (K @ points_cam.T).T  # shape: (N, 3)\n",
+        "    \n",
+        "    # pixel coordinates\n",
+        "    pixels = projected[:, :2] / projected[:, 2:3]  # divide x and y by z\n",
+        "    print(pixels)\n",
+        "    image = camera.image  # shape (H, W, 3), \n",
+        "    img_h, img_w = image.shape[:2]\n",
+        "    valid = (\n",
+        "        (pixels[:, 0] >= 0) & (pixels[:, 0] < img_w) &\n",
+        "        (pixels[:, 1] >= 0) & (pixels[:, 1] < img_h)\n",
+        "    )\n",
+        "    pixels_clipped = pixels[valid]\n",
+        "    return pixels_clipped\n",
+        "    \n",
+        "def plot_traj_img(scene, pixels_clipped, cam='front',ground_truth_clipped=None):\n",
+        "    frame_idx = scene.scene_metadata.num_history_frames - 1\n",
+        "    if cam == 'front':\n",
+        "        camera = scene.frames[frame_idx].cameras.cam_f0\n",
+        "    elif cam == 'left':\n",
+        "        camera = scene.frames[frame_idx].cameras.cam_l0\n",
+        "    else:\n",
+        "        camera = scene.frames[frame_idx].cameras.cam_r0\n",
+        "    image = camera.image\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    plt.imshow(image, alpha=0.6)\n",
+        "    plt.scatter(pixels_clipped[:, 0], pixels_clipped[:, 1], c='red', s=40, marker='o')\n",
+        "    plt.plot(pixels_clipped[:,0],pixels_clipped[:,1], color='red', linewidth=2, marker='o', markersize=5)\n",
+        "    #gt \n",
+        "    if ground_truth_clipped is not None:\n",
+        "        plt.scatter(ground_truth_clipped[:, 0], ground_truth_clipped[:, 1], c='green', s=40, marker='o')\n",
+        "        plt.plot(ground_truth_clipped[:,0],ground_truth_clipped[:,1], color='green', linewidth=2, marker='o', markersize=5)\n",
+        "    \n",
+        "    plt.title(\"Projected Waypoints onto Camera Image\")\n",
+        "    plt.axis(\"off\")\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "    \n",
+        "# example use\n",
+        "# pixels_clipped = get_pixels(scene, agent)\n",
+        "# gt = get_pixels(scene, agent,cam='front',human=True)\n",
+        "# plot_traj_img(scene, pixels_clipped, cam='front',ground_truth_clipped=gt)\n",
+        "\n",
+        "# pixels_clipped = get_pixels(scene, agent,'left')\n",
+        "# gt = get_pixels(scene, agent,cam='left',human=True)\n",
+        "\n",
+        "# plot_traj_img(scene, pixels_clipped, cam='left',ground_truth_clipped=gt)\n",
+        "\n",
+        "# print(pixels_clipped)\n",
+        "# Plot\n"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},