diff --git a/pcdet/datasets/augmentor/augmentor_utils.py b/pcdet/datasets/augmentor/augmentor_utils.py
new file mode 100644
index 0000000..3c088e3
--- /dev/null
+++ b/pcdet/datasets/augmentor/augmentor_utils.py
@@ -0,0 +1,658 @@
+import numpy as np
+import math
+import copy
+from ...utils import common_utils
+from ...utils import box_utils
+
+
+def random_flip_along_x(gt_boxes, points, return_flip=False, enable=None):
+    """
+    Args:
+        gt_boxes: (N, 7 + C), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C)
+    Returns:
+    """
+    if enable is None:
+        enable = np.random.choice([False, True], replace=False, p=[0.5, 0.5])
+    if enable:
+        gt_boxes[:, 1] = -gt_boxes[:, 1]
+        gt_boxes[:, 6] = -gt_boxes[:, 6]
+        points[:, 1] = -points[:, 1]
+        
+        if gt_boxes.shape[1] > 7:
+            gt_boxes[:, 8] = -gt_boxes[:, 8]
+    if return_flip:
+        return gt_boxes, points, enable
+    return gt_boxes, points
+
+
+def random_flip_along_y(gt_boxes, points, return_flip=False, enable=None):
+    """
+    Args:
+        gt_boxes: (N, 7 + C), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C)
+    Returns:
+    """
+    if enable is None:
+        enable = np.random.choice([False, True], replace=False, p=[0.5, 0.5])
+    if enable:
+        gt_boxes[:, 0] = -gt_boxes[:, 0]
+        gt_boxes[:, 6] = -(gt_boxes[:, 6] + np.pi)
+        points[:, 0] = -points[:, 0]
+
+        if gt_boxes.shape[1] > 7:
+            gt_boxes[:, 7] = -gt_boxes[:, 7]
+    if return_flip:
+        return gt_boxes, points, enable
+    return gt_boxes, points
+
+
+def global_rotation(gt_boxes, points, rot_range, return_rot=False, noise_rotation=None):
+    """
+    Args:
+        gt_boxes: (N, 7 + C), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C),
+        rot_range: [min, max]
+    Returns:
+    """
+    if noise_rotation is None: 
+        noise_rotation = np.random.uniform(rot_range[0], rot_range[1])
+    points = common_utils.rotate_points_along_z(points[np.newaxis, :, :], np.array([noise_rotation]))[0]
+    gt_boxes[:, 0:3] = common_utils.rotate_points_along_z(gt_boxes[np.newaxis, :, 0:3], np.array([noise_rotation]))[0]
+    gt_boxes[:, 6] += noise_rotation
+    if gt_boxes.shape[1] > 7:
+        gt_boxes[:, 7:9] = common_utils.rotate_points_along_z(
+            np.hstack((gt_boxes[:, 7:9], np.zeros((gt_boxes.shape[0], 1))))[np.newaxis, :, :],
+            np.array([noise_rotation])
+        )[0][:, 0:2]
+
+    if return_rot:
+        return gt_boxes, points, noise_rotation
+    return gt_boxes, points
+
+
+def global_scaling(gt_boxes, points, scale_range, return_scale=False):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading]
+        points: (M, 3 + C),
+        scale_range: [min, max]
+    Returns:
+    """
+    if scale_range[1] - scale_range[0] < 1e-3:
+        return gt_boxes, points
+    noise_scale = np.random.uniform(scale_range[0], scale_range[1])
+    points[:, :3] *= noise_scale
+    gt_boxes[:, :6] *= noise_scale
+    if gt_boxes.shape[1] > 7:
+        gt_boxes[:, 7:] *= noise_scale
+        
+    if return_scale:
+        return gt_boxes, points, noise_scale
+    return gt_boxes, points
+
+def global_scaling_with_roi_boxes(gt_boxes, roi_boxes, points, scale_range, return_scale=False):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading]
+        points: (M, 3 + C),
+        scale_range: [min, max]
+    Returns:
+    """
+    if scale_range[1] - scale_range[0] < 1e-3:
+        return gt_boxes, points
+    noise_scale = np.random.uniform(scale_range[0], scale_range[1])
+    points[:, :3] *= noise_scale
+    gt_boxes[:, :6] *= noise_scale
+    roi_boxes[:,:, [0,1,2,3,4,5,7,8]] *= noise_scale
+    if return_scale:
+        return gt_boxes,roi_boxes, points, noise_scale
+    return gt_boxes, roi_boxes, points
+
+
+def random_image_flip_horizontal(image, depth_map, gt_boxes, calib):
+    """
+    Performs random horizontal flip augmentation
+    Args:
+        image: (H_image, W_image, 3), Image
+        depth_map: (H_depth, W_depth), Depth map
+        gt_boxes: (N, 7), 3D box labels in LiDAR coordinates [x, y, z, w, l, h, ry]
+        calib: calibration.Calibration, Calibration object
+    Returns:
+        aug_image: (H_image, W_image, 3), Augmented image
+        aug_depth_map: (H_depth, W_depth), Augmented depth map
+        aug_gt_boxes: (N, 7), Augmented 3D box labels in LiDAR coordinates [x, y, z, w, l, h, ry]
+    """
+    # Randomly augment with 50% chance
+    enable = np.random.choice([False, True], replace=False, p=[0.5, 0.5])
+
+    if enable:
+        # Flip images
+        aug_image = np.fliplr(image)
+        aug_depth_map = np.fliplr(depth_map)
+        
+        # Flip 3D gt_boxes by flipping the centroids in image space
+        aug_gt_boxes = copy.copy(gt_boxes)
+        locations = aug_gt_boxes[:, :3]
+        img_pts, img_depth = calib.lidar_to_img(locations)
+        W = image.shape[1]
+        img_pts[:, 0] = W - img_pts[:, 0]
+        pts_rect = calib.img_to_rect(u=img_pts[:, 0], v=img_pts[:, 1], depth_rect=img_depth)
+        pts_lidar = calib.rect_to_lidar(pts_rect)
+        aug_gt_boxes[:, :3] = pts_lidar
+        aug_gt_boxes[:, 6] = -1 * aug_gt_boxes[:, 6]
+
+    else:
+        aug_image = image
+        aug_depth_map = depth_map
+        aug_gt_boxes = gt_boxes
+
+    return aug_image, aug_depth_map, aug_gt_boxes
+
+
+def random_local_translation_along_x(gt_boxes, points, offset_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C),
+        offset_range: [min max]]
+    Returns:
+    """
+    # augs = {}
+    for idx, box in enumerate(gt_boxes):
+        offset = np.random.uniform(offset_range[0], offset_range[1])
+        # augs[f'object_{idx}'] = offset
+        points_in_box, mask = get_points_in_box(points, box)
+        points[mask, 0] += offset
+        
+        gt_boxes[idx, 0] += offset
+    
+        # if gt_boxes.shape[1] > 7:
+        #     gt_boxes[idx, 7] += offset
+    
+    return gt_boxes, points
+
+
+def random_local_translation_along_y(gt_boxes, points, offset_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C),
+        offset_range: [min max]]
+    Returns:
+    """
+    # augs = {}
+    for idx, box in enumerate(gt_boxes):
+        offset = np.random.uniform(offset_range[0], offset_range[1])
+        # augs[f'object_{idx}'] = offset
+        points_in_box, mask = get_points_in_box(points, box)
+        points[mask, 1] += offset
+        
+        gt_boxes[idx, 1] += offset
+    
+        # if gt_boxes.shape[1] > 8:
+        #     gt_boxes[idx, 8] += offset
+    
+    return gt_boxes, points
+
+
+def random_local_translation_along_z(gt_boxes, points, offset_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C),
+        offset_range: [min max]]
+    Returns:
+    """
+    # augs = {}
+    for idx, box in enumerate(gt_boxes):
+        offset = np.random.uniform(offset_range[0], offset_range[1])
+        # augs[f'object_{idx}'] = offset
+        points_in_box, mask = get_points_in_box(points, box)
+        points[mask, 2] += offset
+        
+        gt_boxes[idx, 2] += offset
+    
+    return gt_boxes, points
+
+
+def global_frustum_dropout_top(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+    # threshold = max - length * uniform(0 ~ 0.2)
+    threshold = np.max(points[:, 2]) - intensity * (np.max(points[:, 2]) - np.min(points[:, 2]))
+    
+    points = points[points[:, 2] < threshold]
+    gt_boxes = gt_boxes[gt_boxes[:, 2] < threshold]
+    return gt_boxes, points
+
+
+def global_frustum_dropout_bottom(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+    
+    threshold = np.min(points[:, 2]) + intensity * (np.max(points[:, 2]) - np.min(points[:, 2]))
+    points = points[points[:, 2] > threshold]
+    gt_boxes = gt_boxes[gt_boxes[:, 2] > threshold]
+    
+    return gt_boxes, points
+
+
+def global_frustum_dropout_left(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+    
+    threshold = np.max(points[:, 1]) - intensity * (np.max(points[:, 1]) - np.min(points[:, 1]))
+    points = points[points[:, 1] < threshold]
+    gt_boxes = gt_boxes[gt_boxes[:, 1] < threshold]
+    
+    return gt_boxes, points
+
+
+def global_frustum_dropout_right(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+    
+    threshold = np.min(points[:, 1]) + intensity * (np.max(points[:, 1]) - np.min(points[:, 1]))
+    points = points[points[:, 1] > threshold]
+    gt_boxes = gt_boxes[gt_boxes[:, 1] > threshold]
+    
+    return gt_boxes, points
+
+
+def local_scaling(gt_boxes, points, scale_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading]
+        points: (M, 3 + C),
+        scale_range: [min, max]
+    Returns:
+    """
+    if scale_range[1] - scale_range[0] < 1e-3:
+        return gt_boxes, points
+    
+    # augs = {}
+    for idx, box in enumerate(gt_boxes):
+        noise_scale = np.random.uniform(scale_range[0], scale_range[1])
+        # augs[f'object_{idx}'] = noise_scale
+        points_in_box, mask = get_points_in_box(points, box)
+        
+        # tranlation to axis center
+        points[mask, 0] -= box[0]
+        points[mask, 1] -= box[1]
+        points[mask, 2] -= box[2]
+        
+        # apply scaling
+        points[mask, :3] *= noise_scale
+        
+        # tranlation back to original position
+        points[mask, 0] += box[0]
+        points[mask, 1] += box[1]
+        points[mask, 2] += box[2]
+        
+        gt_boxes[idx, 3:6] *= noise_scale
+    return gt_boxes, points
+
+
+def local_rotation(gt_boxes, points, rot_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C),
+        rot_range: [min, max]
+    Returns:
+    """
+    # augs = {}
+    for idx, box in enumerate(gt_boxes):
+        noise_rotation = np.random.uniform(rot_range[0], rot_range[1])
+        # augs[f'object_{idx}'] = noise_rotation
+        points_in_box, mask = get_points_in_box(points, box)
+        
+        centroid_x = box[0]
+        centroid_y = box[1]
+        centroid_z = box[2]
+        
+        # tranlation to axis center
+        points[mask, 0] -= centroid_x
+        points[mask, 1] -= centroid_y
+        points[mask, 2] -= centroid_z
+        box[0] -= centroid_x
+        box[1] -= centroid_y
+        box[2] -= centroid_z
+        
+        # apply rotation
+        points[mask, :] = common_utils.rotate_points_along_z(points[np.newaxis, mask, :], np.array([noise_rotation]))[0]
+        box[0:3] = common_utils.rotate_points_along_z(box[np.newaxis, np.newaxis, 0:3], np.array([noise_rotation]))[0][0]
+        
+        # tranlation back to original position
+        points[mask, 0] += centroid_x
+        points[mask, 1] += centroid_y
+        points[mask, 2] += centroid_z
+        box[0] += centroid_x
+        box[1] += centroid_y
+        box[2] += centroid_z
+        
+        gt_boxes[idx, 6] += noise_rotation
+        if gt_boxes.shape[1] > 8:
+            gt_boxes[idx, 7:9] = common_utils.rotate_points_along_z(
+                np.hstack((gt_boxes[idx, 7:9], np.zeros((gt_boxes.shape[0], 1))))[np.newaxis, :, :],
+                np.array([noise_rotation])
+            )[0][:, 0:2]
+    
+    return gt_boxes, points
+
+
+def local_frustum_dropout_top(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    for idx, box in enumerate(gt_boxes):
+        x, y, z, dx, dy, dz = box[0], box[1], box[2], box[3], box[4], box[5]
+        
+        intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+        points_in_box, mask = get_points_in_box(points, box)
+        threshold = (z + dz / 2) - intensity * dz
+        
+        points = points[np.logical_not(np.logical_and(mask, points[:, 2] >= threshold))]
+    
+    return gt_boxes, points
+
+
+def local_frustum_dropout_bottom(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    for idx, box in enumerate(gt_boxes):
+        x, y, z, dx, dy, dz = box[0], box[1], box[2], box[3], box[4], box[5]
+        
+        intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+        points_in_box, mask = get_points_in_box(points, box)
+        threshold = (z - dz / 2) + intensity * dz
+        
+        points = points[np.logical_not(np.logical_and(mask, points[:, 2] <= threshold))]
+    
+    return gt_boxes, points
+
+
+def local_frustum_dropout_left(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    for idx, box in enumerate(gt_boxes):
+        x, y, z, dx, dy, dz = box[0], box[1], box[2], box[3], box[4], box[5]
+        
+        intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+        points_in_box, mask = get_points_in_box(points, box)
+        threshold = (y + dy / 2) - intensity * dy
+        
+        points = points[np.logical_not(np.logical_and(mask, points[:, 1] >= threshold))]
+    
+    return gt_boxes, points
+
+
+def local_frustum_dropout_right(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    for idx, box in enumerate(gt_boxes):
+        x, y, z, dx, dy, dz = box[0], box[1], box[2], box[3], box[4], box[5]
+        
+        intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+        points_in_box, mask = get_points_in_box(points, box)
+        threshold = (y - dy / 2) + intensity * dy
+        
+        points = points[np.logical_not(np.logical_and(mask, points[:, 1] <= threshold))]
+    
+    return gt_boxes, points
+
+
+def get_points_in_box(points, gt_box):
+    x, y, z = points[:, 0], points[:, 1], points[:, 2]
+    cx, cy, cz = gt_box[0], gt_box[1], gt_box[2]
+    dx, dy, dz, rz = gt_box[3], gt_box[4], gt_box[5], gt_box[6]
+    shift_x, shift_y, shift_z = x - cx, y - cy, z - cz
+    
+    MARGIN = 1e-1
+    cosa, sina = math.cos(-rz), math.sin(-rz)
+    local_x = shift_x * cosa + shift_y * (-sina)
+    local_y = shift_x * sina + shift_y * cosa
+    
+    mask = np.logical_and(abs(shift_z) <= dz / 2.0, 
+                          np.logical_and(abs(local_x) <= dx / 2.0 + MARGIN, 
+                                         abs(local_y) <= dy / 2.0 + MARGIN))
+    
+    points = points[mask]
+    
+    return points, mask
+
+
+def get_pyramids(boxes):
+    pyramid_orders = np.array([
+        [0, 1, 5, 4],
+        [4, 5, 6, 7],
+        [7, 6, 2, 3],
+        [3, 2, 1, 0],
+        [1, 2, 6, 5],
+        [0, 4, 7, 3]
+    ])
+    boxes_corners = box_utils.boxes_to_corners_3d(boxes).reshape(-1, 24)
+    
+    pyramid_list = []
+    for order in pyramid_orders:
+        # frustum polygon: 5 corners, 5 surfaces
+        pyramid = np.concatenate((
+            boxes[:, 0:3],
+            boxes_corners[:, 3 * order[0]: 3 * order[0] + 3],
+            boxes_corners[:, 3 * order[1]: 3 * order[1] + 3],
+            boxes_corners[:, 3 * order[2]: 3 * order[2] + 3],
+            boxes_corners[:, 3 * order[3]: 3 * order[3] + 3]), axis=1)
+        pyramid_list.append(pyramid[:, None, :])
+    pyramids = np.concatenate(pyramid_list, axis=1)  # [N, 6, 15], 15=5*3
+    return pyramids
+
+
+def one_hot(x, num_class=1):
+    if num_class is None:
+        num_class = 1
+    ohx = np.zeros((len(x), num_class))
+    ohx[range(len(x)), x] = 1
+    return ohx
+
+
+def points_in_pyramids_mask(points, pyramids):
+    pyramids = pyramids.reshape(-1, 5, 3)
+    flags = np.zeros((points.shape[0], pyramids.shape[0]), dtype=np.bool)
+    for i, pyramid in enumerate(pyramids):
+        flags[:, i] = np.logical_or(flags[:, i], box_utils.in_hull(points[:, 0:3], pyramid))
+    return flags
+
+
+def local_pyramid_dropout(gt_boxes, points, dropout_prob, pyramids=None):
+    if pyramids is None:
+        pyramids = get_pyramids(gt_boxes).reshape([-1, 6, 5, 3])  # each six surface of boxes: [num_boxes, 6, 15=3*5]
+    drop_pyramid_indices = np.random.randint(0, 6, (pyramids.shape[0]))
+    drop_pyramid_one_hot = one_hot(drop_pyramid_indices, num_class=6)
+    drop_box_mask = np.random.uniform(0, 1, (pyramids.shape[0])) <= dropout_prob
+    if np.sum(drop_box_mask) != 0:
+        drop_pyramid_mask = (np.tile(drop_box_mask[:, None], [1, 6]) * drop_pyramid_one_hot) > 0
+        drop_pyramids = pyramids[drop_pyramid_mask]
+        point_masks = points_in_pyramids_mask(points, drop_pyramids)
+        points = points[np.logical_not(point_masks.any(-1))]
+    # print(drop_box_mask)
+    pyramids = pyramids[np.logical_not(drop_box_mask)]
+    return gt_boxes, points, pyramids
+
+
+def local_pyramid_sparsify(gt_boxes, points, prob, max_num_pts, pyramids=None):
+    if pyramids is None:
+        pyramids = get_pyramids(gt_boxes).reshape([-1, 6, 5, 3])  # each six surface of boxes: [num_boxes, 6, 15=3*5]
+    if pyramids.shape[0] > 0:
+        sparsity_prob, sparsity_num = prob, max_num_pts
+        sparsify_pyramid_indices = np.random.randint(0, 6, (pyramids.shape[0]))
+        sparsify_pyramid_one_hot = one_hot(sparsify_pyramid_indices, num_class=6)
+        sparsify_box_mask = np.random.uniform(0, 1, (pyramids.shape[0])) <= sparsity_prob
+        sparsify_pyramid_mask = (np.tile(sparsify_box_mask[:, None], [1, 6]) * sparsify_pyramid_one_hot) > 0
+        # print(sparsify_box_mask)
+        
+        pyramid_sampled = pyramids[sparsify_pyramid_mask]  # (-1,6,5,3)[(num_sample,6)]
+        # print(pyramid_sampled.shape)
+        pyramid_sampled_point_masks = points_in_pyramids_mask(points, pyramid_sampled)
+        pyramid_sampled_points_num = pyramid_sampled_point_masks.sum(0)  # the number of points in each surface pyramid
+        valid_pyramid_sampled_mask = pyramid_sampled_points_num > sparsity_num  # only much than sparsity_num should be sparse
+        
+        sparsify_pyramids = pyramid_sampled[valid_pyramid_sampled_mask]
+        if sparsify_pyramids.shape[0] > 0:
+            point_masks = pyramid_sampled_point_masks[:, valid_pyramid_sampled_mask]
+            remain_points = points[
+                np.logical_not(point_masks.any(-1))]  # points which outside the down sampling pyramid
+            to_sparsify_points = [points[point_masks[:, i]] for i in range(point_masks.shape[1])]
+            
+            sparsified_points = []
+            for sample in to_sparsify_points:
+                sampled_indices = np.random.choice(sample.shape[0], size=sparsity_num, replace=False)
+                sparsified_points.append(sample[sampled_indices])
+            sparsified_points = np.concatenate(sparsified_points, axis=0)
+            points = np.concatenate([remain_points, sparsified_points], axis=0)
+        pyramids = pyramids[np.logical_not(sparsify_box_mask)]
+    return gt_boxes, points, pyramids
+
+
+def local_pyramid_swap(gt_boxes, points, prob, max_num_pts, pyramids=None):
+    def get_points_ratio(points, pyramid):
+        surface_center = (pyramid[3:6] + pyramid[6:9] + pyramid[9:12] + pyramid[12:]) / 4.0
+        vector_0, vector_1, vector_2 = pyramid[6:9] - pyramid[3:6], pyramid[12:] - pyramid[3:6], pyramid[0:3] - surface_center
+        alphas = ((points[:, 0:3] - pyramid[3:6]) * vector_0).sum(-1) / np.power(vector_0, 2).sum()
+        betas = ((points[:, 0:3] - pyramid[3:6]) * vector_1).sum(-1) / np.power(vector_1, 2).sum()
+        gammas = ((points[:, 0:3] - surface_center) * vector_2).sum(-1) / np.power(vector_2, 2).sum()
+        return [alphas, betas, gammas]
+    
+    def recover_points_by_ratio(points_ratio, pyramid):
+        alphas, betas, gammas = points_ratio
+        surface_center = (pyramid[3:6] + pyramid[6:9] + pyramid[9:12] + pyramid[12:]) / 4.0
+        vector_0, vector_1, vector_2 = pyramid[6:9] - pyramid[3:6], pyramid[12:] - pyramid[3:6], pyramid[0:3] - surface_center
+        points = (alphas[:, None] * vector_0 + betas[:, None] * vector_1) + pyramid[3:6] + gammas[:, None] * vector_2
+        return points
+    
+    def recover_points_intensity_by_ratio(points_intensity_ratio, max_intensity, min_intensity):
+        return points_intensity_ratio * (max_intensity - min_intensity) + min_intensity
+    
+    # swap partition
+    if pyramids is None:
+        pyramids = get_pyramids(gt_boxes).reshape([-1, 6, 5, 3])  # each six surface of boxes: [num_boxes, 6, 15=3*5]
+    swap_prob, num_thres = prob, max_num_pts
+    swap_pyramid_mask = np.random.uniform(0, 1, (pyramids.shape[0])) <= swap_prob
+    
+    if swap_pyramid_mask.sum() > 0:
+        point_masks = points_in_pyramids_mask(points, pyramids)
+        point_nums = point_masks.sum(0).reshape(pyramids.shape[0], -1)  # [N, 6]
+        non_zero_pyramids_mask = point_nums > num_thres  # ingore dropout pyramids or highly occluded pyramids
+        selected_pyramids = non_zero_pyramids_mask * swap_pyramid_mask[:,
+                                                     None]  # selected boxes and all their valid pyramids
+        # print(selected_pyramids)
+        if selected_pyramids.sum() > 0:
+            # get to_swap pyramids
+            index_i, index_j = np.nonzero(selected_pyramids)
+            selected_pyramid_indices = [np.random.choice(index_j[index_i == i]) \
+                                            if e and (index_i == i).any() else 0 for i, e in
+                                        enumerate(swap_pyramid_mask)]
+            selected_pyramids_mask = selected_pyramids * one_hot(selected_pyramid_indices, num_class=6) == 1
+            to_swap_pyramids = pyramids[selected_pyramids_mask]
+            
+            # get swapped pyramids
+            index_i, index_j = np.nonzero(selected_pyramids_mask)
+            non_zero_pyramids_mask[selected_pyramids_mask] = False
+            swapped_index_i = np.array([np.random.choice(np.where(non_zero_pyramids_mask[:, j])[0]) if \
+                                            np.where(non_zero_pyramids_mask[:, j])[0].shape[0] > 0 else
+                                        index_i[i] for i, j in enumerate(index_j.tolist())])
+            swapped_indicies = np.concatenate([swapped_index_i[:, None], index_j[:, None]], axis=1)
+            swapped_pyramids = pyramids[
+                swapped_indicies[:, 0].astype(np.int32), swapped_indicies[:, 1].astype(np.int32)]
+            
+            # concat to_swap&swapped pyramids
+            swap_pyramids = np.concatenate([to_swap_pyramids, swapped_pyramids], axis=0)
+            swap_point_masks = points_in_pyramids_mask(points, swap_pyramids)
+            remain_points = points[np.logical_not(swap_point_masks.any(-1))]
+            
+            # swap pyramids
+            points_res = []
+            num_swapped_pyramids = swapped_pyramids.shape[0]
+            for i in range(num_swapped_pyramids):
+                to_swap_pyramid = to_swap_pyramids[i]
+                swapped_pyramid = swapped_pyramids[i]
+                
+                to_swap_points = points[swap_point_masks[:, i]]
+                swapped_points = points[swap_point_masks[:, i + num_swapped_pyramids]]
+                # for intensity transform
+                to_swap_points_intensity_ratio = (to_swap_points[:, -1:] - to_swap_points[:, -1:].min()) / \
+                                                 np.clip(
+                                                     (to_swap_points[:, -1:].max() - to_swap_points[:, -1:].min()),
+                                                     1e-6, 1)
+                swapped_points_intensity_ratio = (swapped_points[:, -1:] - swapped_points[:, -1:].min()) / \
+                                                 np.clip(
+                                                     (swapped_points[:, -1:].max() - swapped_points[:, -1:].min()),
+                                                     1e-6, 1)
+                
+                to_swap_points_ratio = get_points_ratio(to_swap_points, to_swap_pyramid.reshape(15))
+                swapped_points_ratio = get_points_ratio(swapped_points, swapped_pyramid.reshape(15))
+                new_to_swap_points = recover_points_by_ratio(swapped_points_ratio, to_swap_pyramid.reshape(15))
+                new_swapped_points = recover_points_by_ratio(to_swap_points_ratio, swapped_pyramid.reshape(15))
+                # for intensity transform
+                new_to_swap_points_intensity = recover_points_intensity_by_ratio(
+                    swapped_points_intensity_ratio, to_swap_points[:, -1:].max(),
+                    to_swap_points[:, -1:].min())
+                new_swapped_points_intensity = recover_points_intensity_by_ratio(
+                    to_swap_points_intensity_ratio, swapped_points[:, -1:].max(),
+                    swapped_points[:, -1:].min())
+                
+                # new_to_swap_points = np.concatenate([new_to_swap_points, swapped_points[:, -1:]], axis=1)
+                # new_swapped_points = np.concatenate([new_swapped_points, to_swap_points[:, -1:]], axis=1)
+                
+                new_to_swap_points = np.concatenate([new_to_swap_points, new_to_swap_points_intensity], axis=1)
+                new_swapped_points = np.concatenate([new_swapped_points, new_swapped_points_intensity], axis=1)
+                
+                points_res.append(new_to_swap_points)
+                points_res.append(new_swapped_points)
+            
+            points_res = np.concatenate(points_res, axis=0)
+            points = np.concatenate([remain_points, points_res], axis=0)
+    return gt_boxes, points