diff --git a/scripts/dataset/pre_lightstage.py b/scripts/dataset/pre_lightstage.py
new file mode 100644
index 0000000..f5073e9
--- /dev/null
+++ b/scripts/dataset/pre_lightstage.py
@@ -0,0 +1,552 @@
+import re
+import numpy as np
+import os, sys
+import cv2
+import shutil
+from os.path import join
+from tqdm import trange, tqdm
+from multiprocessing import Pool
+import json
+
+def save_json(file, data):
+    if not os.path.exists(os.path.dirname(file)):
+        os.makedirs(os.path.dirname(file))
+    with open(file, 'w') as f:
+        json.dump(data, f, indent=4)
+
+def parseImg(imgname):
+    """ 解析图像名称
+    
+    Arguments:
+        imgname {str} -- 
+    
+    Returns:
+        dic -- 包含文件图像信息的字典
+    """
+    s = re.search(
+        '(?P<id>\d+)_(?P<year>\d{4})-(?P<month>\d{2})-(?P<day>\d{2})_(?P<hour>\d{2})-(?P<min>\d{2})-(?P<sec>\d{2})\.(?P<ms>\d{3})',
+        imgname)
+    assert s is not None, imgname
+    dic = s.groupdict()
+    for key in dic.keys():
+        dic[key] = int(dic[key])
+    dic['time'] = dic['ms'] + dic['sec'] * 1000 + dic['min'] * 60000 + dic['hour'] * 60000 * 60
+    return dic
+
+
+def getCamNum(x):
+    return x.split('_B')[1]
+
+
+def getImgId(x):
+    return x.split('_')[4]
+
+
+def findBeginEnd(images_info):
+    begin_time = 0
+    end_time = np.inf
+    for key in images_info:
+        first_frame = images_info[key]['first_frame']
+        last_frame = images_info[key]['last_frame']
+        curr_f_time = images_info[key][first_frame]['time']
+        curr_e_time = images_info[key][last_frame]['time']
+        if curr_f_time > begin_time:
+            begin_time = curr_f_time
+        if curr_e_time < end_time:
+            end_time = curr_e_time
+    return begin_time, end_time
+
+
+def findRef(images_info):
+    ref_cam = 0
+    min_frame = np.inf
+
+    for key in images_info:
+        first_frame = images_info[key]['first_frame']
+        last_frame = images_info[key]['last_frame']
+        f_id = images_info[key][first_frame]['id']
+        e_id = images_info[key][last_frame]['id']
+        if (e_id - f_id) < min_frame:
+            min_frame = e_id - f_id
+            ref_cam = key
+    return ref_cam
+
+
+def findNearest(cam_info, time):
+    # 找time最接近的帧的名称
+    select_frame = ''
+    # WARN: 确保cam_info是有序的
+    img_pre = None
+    for idx, img in enumerate(cam_info.keys()):
+        if isinstance(cam_info[img], dict):
+            if cam_info[img]['time'] < time:
+                img_pre = img
+                continue
+            else:
+                select_frame = img
+                break
+    # 判断一下处于边界上的两帧，哪一帧的时间更接近
+    if img_pre is not None:
+        if abs(time - cam_info[img_pre]['time']) < abs(time - cam_info[img]['time']):
+            select_frame = img_pre
+    return select_frame
+
+
+def get_filelists(path, save_path):
+    cameralists = sorted(os.listdir(path), key=lambda x: getCamNum(x))
+    images_info = {}
+    for camname in cameralists:
+        images_info[camname] = {}
+        imglists = listdir([path, camname])
+        imglists.sort(key=lambda x: getImgId(x))
+        for imgname in tqdm(imglists, desc=camname):
+            images_info[camname][imgname] = parseImg(imgname)
+        images_info[camname]['first_frame'] = imglists[0]
+        images_info[camname]['last_frame'] = imglists[-1]
+        # print(images_info[camname])
+    # 寻找最晚开始最早结束的时间
+    begin_time, end_time = findBeginEnd(images_info)
+    print('begin time: {}, end time: {}'.format(begin_time, end_time))
+    # 寻找帧率最低的视频，以这个视频为参考
+    if args.ref is None:
+        ref_cam = findRef(images_info)
+    else:
+        ref_cam = args.ref
+
+    print('The reference camera is {}'.format(ref_cam))
+    # 以帧率最低的相机为参考，对每一帧寻找其他相机时间最接近的帧
+    output_info = {key: [] for key in cameralists}
+    for imgname in tqdm(images_info[ref_cam].keys(), 'sync'):
+        if isinstance(images_info[ref_cam][imgname], dict):
+            cur_time = images_info[ref_cam][imgname]['time']
+            if cur_time < begin_time:
+                continue
+            if cur_time > end_time:
+                break
+            for cam in cameralists:
+                if cam == ref_cam:
+                    select = imgname
+                else:
+                    select = findNearest(images_info[cam], cur_time)
+                output_info[cam].append(select)
+    # 将图片保存
+    mkdir(save_path)
+    # 保存匹配信息
+    # TODO:增加匹配时间差的指标
+    import json
+    with open(join(save_path, 'match_info.json'), 'w') as f:
+        json.dump(output_info, f, indent=4)
+    for cam in cameralists:
+        mkdir(join(save_path, cam))
+        for i, imgname in enumerate(tqdm(output_info[cam], desc=cam)):
+            src = join(path, cam, imgname)
+            dst = join(save_path, cam, '%06d.jpg' % i)
+            img = cv2.imread(src)
+            if img.shape[0] == 2048:
+                img = cv2.resize(img, (1024, 1024), cv2.INTER_NEAREST)
+            cv2.imwrite(dst, img)
+
+def getFileDict(path):
+    cams = sorted(os.listdir(path))
+    cams = [cam for cam in cams if os.path.isdir(join(path, cam))]
+    cams = list(filter(
+        lambda x:\
+            x.startswith('Camera')\
+            and x not in filter_list
+            , cams)) # B6相机同步有问题 不要使用了
+    results = {}
+    for cam in cams:
+        # 注意：lightstage的图像直接sort就是按照顺序了的
+        files = sorted(os.listdir(join(path, cam)))
+        files = [f for f in files if f.endswith('.jpg')]
+        results[cam] = files
+    return cams, results
+
+def sync_by_name(imagelists, times_all, cams):
+    # 选择开始帧
+    start = max([t[0] for t in times_all.values()])
+    # 弹出开始帧以前的数据
+    for cam in cams:
+        times = times_all[cam].tolist()
+        while times[0] < start:
+            times.pop(0)
+            imagelists[cam].pop(0)
+        times_all[cam] = np.array(times)
+    # 选择参考视角的时候,应该选择与其他视角的距离最近的作为参考
+    best_distances = []
+    for cam in cams:
+        # 分别对每个进行设置, 使用第一帧的时间,留有余地
+        ref_time = times_all[cam][1]
+        distances = []
+        for c in cams:
+            dist = np.abs(times_all[c] - ref_time).min()
+            distances.append(dist)
+        print('{:10s}: {:.2f}'.format(cam, sum(distances)/len(cams)))
+        best_distances.append(sum(distances)/len(cams))
+    best_distances = np.array(best_distances)
+    ref_view = best_distances.argmin()
+    if args.ref is None:
+        ref_cam = cams[best_distances.argmin()]
+    else:
+        ref_cam = args.ref
+        ref_view = cams.index(ref_cam)
+
+    times_all = [times_all[cam] for cam in cams]
+    print('Select reference view: ', cams[ref_view])
+    if False:
+        distance = np.eye((dimGroups[-1]))
+        for nv0 in range(len(times_all)-1):
+            for nv1 in range(nv0+1, len(times_all)):
+                dist = np.abs(times_all[nv0][:, None] - times_all[nv1][None, :])
+                dist = (MAX_DIST - dist)/MAX_DIST
+                dist[dist<0] = 0
+                distance[dimGroups[nv0]:dimGroups[nv0+1], dimGroups[nv1]:dimGroups[nv1+1]] = dist
+                distance[dimGroups[nv1]:dimGroups[nv1+1], dimGroups[nv0]:dimGroups[nv0+1]] = dist.T
+        matched, ref_view = match_dtw(distance, dimGroups, debug=args.debug)
+    elif True:
+        # 使用最近邻选择
+        matched = []
+        for nv in range(len(times_all)):
+            dist = np.abs(times_all[ref_view][:, None] - times_all[nv][None, :])
+            rows = np.arange(dist.shape[0])
+            argmin0 = dist.argmin(axis=1)
+            # 直接选择最近的吧
+            # 去掉开头
+            for i in range(argmin0.shape[0]):
+                if argmin0[i] == argmin0[i+1]:
+                    argmin0[i] = -1
+                else:
+                    break
+            # 去掉结尾
+            for i in range(1, argmin0.shape[0]):
+                if argmin0[-i] == argmin0[-i-1]:
+                    argmin0[-i] = -1
+                else:
+                    break
+            matched.append(argmin0)
+        matched = np.stack(matched)
+    elif False:
+        # 1. 首先判断一下所有视角的最接近的点
+        nViews = len(times_all)
+        TIME_STEP = 20
+        REF_OFFSET = 20 # 给参考视角增加一个帧的偏移，保证所有相机都正常开启了，同时增加一个帧的结束，保证所有相机都结束了
+        views_ref = [ref_view]
+        matched = {
+            ref_view:np.arange(REF_OFFSET, times_all[ref_view].shape[0]-REF_OFFSET)
+        }
+        while True:
+            times_mean = np.stack([times_all[ref][matched[ref]] for ref in matched.keys()])
+            times_mean = np.mean(times_mean, axis=0)
+            infos = []
+            for nv in range(nViews):
+                if nv in matched.keys():
+                    continue
+                if False:
+                    dist_all = []
+                    for ref, indices in matched.items():
+                        dist = np.abs(times_all[ref][indices, None] - times_all[nv][None, :])
+                        dist[dist>TIME_STEP] = 10*TIME_STEP
+                        dist_all.append(dist)
+                    dist = np.stack(dist_all).sum(axis=0)
+                    dist = dist / len(matched.keys())
+                else:
+                    dist = np.abs(times_mean[:, None] - times_all[nv][None, :])
+                argmin0 = dist.argmin(axis=1)
+                rows = np.arange(dist.shape[0])
+                dist_sum = dist.min(axis=1).mean()
+                infos.append({
+                    'v': nv,
+                    'dist_sum': dist_sum,
+                    'argmin': argmin0
+                })
+                print(nv, dist_sum)
+            if len(infos) == 0:
+                break
+            infos.sort(key=lambda x:x['dist_sum'])
+            print('Select view: ', infos[0]['v'], infos[0]['dist_sum'])
+            matched[infos[0]['v']] = infos[0]['argmin']
+        matched = np.stack([matched[nv] for nv in range(nViews)])
+    else:
+        # 选择一个开头，计算最佳的偏移
+        # 开始帧：所有的开始帧中的最晚的一帧
+        # 假定恒定帧率，只需要选择一个开头就好了
+        nViews = len(times_all)
+        start_t = max([t[0] for t in times_all])
+        # 留出10帧来操作
+        start_f = [np.where(t>start_t)[0][0] + 10 for t in times_all]
+        start_t = times_all[ref_view][start_f[ref_view]]
+        valid_f = [[np.where(t<start_t)[0][-1],np.where(t>=start_t)[0][0]] for t in times_all]
+        from copy import deepcopy
+        valid_f_copy = deepcopy(valid_f)
+        import matplotlib as mpl
+        mpl.use('TkAgg')
+        import matplotlib.pyplot as plt
+
+        while True:
+            min_v, min_t = -1, 1e10
+            min_info, max_info = [], []
+            max_v, max_t = -1, -1
+            for nv in range(nViews):
+                if len(valid_f[nv]) == 1:
+                    continue
+                # 存在多个的
+                min_info.append({
+                    'v': nv,
+                    't': times_all[nv][valid_f[nv][0]]
+                })
+                max_info.append({
+                    'v': nv,
+                    't': times_all[nv][valid_f[nv][-1]]
+                })
+            # 判断最小和最大的弹出谁
+            min_info.sort(key=lambda x:x['t'])
+            max_info.sort(key=lambda x:-x['t'])
+            if len(min_info) > 1 and len(max_info) > 1:
+                # delta_min = min_info[1]['t'] - min_info[0]['t']
+                # delta_max = max_info[0]['t'] - max_info[1]['t']
+                delta_max = max_info[0]['t'] - start_t
+                delta_min = start_t - min_info[0]['t']
+                if delta_max > delta_min:
+                    valid_f[max_info[0]['v']].pop(-1)
+                else:
+                    valid_f[min_info[0]['v']].pop(0)
+            else:
+                nv = min_info[0]['v']
+                t_min = times_all[nv][valid_f[0]]
+                t_max = times_all[nv][valid_f[1]]
+                delta_min = start_t - t_min
+                delta_max = t_max - start_t
+                if delta_max > delta_min:
+                    valid_f[nv].pop(-1)
+                else:
+                    valid_f[nv].pop(0)
+                break
+        plt.plot([0, nViews], [start_t, start_t])
+        for nv in range(len(valid_f)):
+            if len(valid_f[nv]) > 1:
+                start, end = valid_f[nv]
+                start, end = times_all[nv][start], times_all[nv][end]
+                plt.plot([nv, nv], [start, end])
+            else:
+                start, end = valid_f_copy[nv][0], valid_f_copy[nv][-1]
+                start, end = times_all[nv][start], times_all[nv][end]
+                plt.plot([nv, nv], [start, end])
+                plt.scatter(nv, times_all[nv][valid_f[nv]])
+        plt.show()
+        matched = np.arange(times_all[ref_view].shape[0]).reshape(1, -1).repeat(nViews, 0)
+        matched = np.arange(2).reshape(1, -1).repeat(nViews, 0)
+        start = np.array(valid_f).reshape(-1, 1)
+        matched += start
+        shape = np.array([t.shape[0] for t in times_all]).reshape(-1, 1) - 10
+        matched[matched<0] = -1
+        # matched[matched>shape] = -1
+    matched = matched[:, (matched!=-1).all(axis=0)]
+    matched_time = np.zeros_like(matched)
+    for nv in range(matched.shape[0]):
+        matched_time[nv] = times_all[nv][matched[nv]]
+    max_time = matched_time.max(axis=0)
+    min_time = matched_time.min(axis=0)
+    diff = max_time - min_time
+    step = matched_time[:, 1:] - matched_time[:, :-1]
+    headers = ['camera', 'start', 'end', 'delta_mean', 'delta_min', 'delta_max', 'diff_max', 'diff_min', 'diff_mean']
+    infos = []
+    dist_to_ref_all = 0
+    for nv, cam in enumerate(cams):
+        dist_to_ref = (matched_time[nv] - matched_time[ref_view]).tolist()
+        dist_to_ref_all += np.abs(dist_to_ref).mean()
+        dist_to_ref.sort(key=lambda x: abs(x))
+        infos.append([cam, matched_time[nv, 0], matched_time[nv, -1], step[nv].mean(), step[nv].min(), step[nv].max(), dist_to_ref[-1], dist_to_ref[0], np.abs(np.array(dist_to_ref)).mean()])
+    print(tabulate(infos, headers=headers))
+    # import matplotlib.pyplot as plt
+    # plt.plot(times_all[7][:100])
+    # plt.plot(times_all[ref_view][:100])
+    # plt.show()
+    # import ipdb;ipdb.set_trace()
+    print("Max sync difference = {}ms, Mean max sync difference = {:.1f}ms".format(diff.max(), diff.mean()))
+    print("Mean sync diff : {}".format(dist_to_ref_all/len(cams)))
+    if not args.nocheck: import ipdb;ipdb.set_trace()
+    return matched, matched_time
+
+def copy_func(src, dst):
+    if args.keep2048:
+        shutil.copyfile(src, dst)
+    else:
+        img = cv2.imread(src)
+        img = cv2.resize(img, (1024, 1024))
+        if colors_params is not None:
+            sub = os.path.basename(os.path.dirname(dst))
+            M = colors_params[sub]
+            img = (np.clip((img.astype(np.float32)/255.) @ M, 0., 1.) * 255).astype(np.uint8)
+        cv2.imwrite(dst, img)
+
+def copy_func_batch(src: list, dst: list):
+    assert(len(src) == len(dst))
+    for i in tqdm(range(len(src))):
+        copy_func(src[i], dst[i])
+
+THREAD_CNT = 8
+
+def copy_with_match(path, out, matched, imagelists, cams, multiple_thread = False):
+    print('---')
+    print('Copy {} to {}'.format(path, out))
+    print('---')
+    pad_2 = lambda x:'{:02d}'.format(int(x))
+    remove_cam = lambda x:x.replace('Camera_B', '').replace('Camera_', '').replace('Camera (', '').replace(')', '')
+    cvt_viewname = lambda x:pad_2(remove_cam(x))
+
+    reports = [[] for _ in range(matched.shape[1])]
+    for nv in tqdm(range(matched.shape[0])):
+        outdir = join(out, 'images', cvt_viewname(cams[nv]))
+        if os.path.exists(outdir):
+            if matched.shape[1] == len(os.listdir(outdir)):
+                print('exists enough images')
+                continue
+            else:
+                print('exists not enough images')
+        else:
+            os.makedirs(outdir, exist_ok=True)
+        imgname_old_s = [[] for _ in range(THREAD_CNT)]
+        imgname_new_s = [[] for _ in range(THREAD_CNT)]
+        for nfnew in range(matched.shape[1]):
+            nf = matched[nv, nfnew]
+            imgname_old = join(path, cams[nv], imagelists[cams[nv]][nf])
+            imgname_old_s[nfnew % THREAD_CNT].append(imgname_old)
+            imgname_new_s[nfnew % THREAD_CNT].append(join(outdir, '{:06d}.jpg'.format(nfnew)))
+            reports[nfnew].append(imgname_old)
+        if multiple_thread:
+            import threading
+            threads = []
+            for i in range(THREAD_CNT):
+                thread = threading.Thread(target=copy_func_batch, args=(imgname_old_s[i], imgname_new_s[i])) # 应该不存在任何数据竞争
+                thread.start()
+                threads.append(thread)
+            for thread in threads:
+                thread.join()
+        else:
+            for nfnew in tqdm(range(matched.shape[1]), desc='{}'.format(cams[nv])):
+                nf = matched[nv, nfnew]
+                imgname_old = join(path, cams[nv], imagelists[cams[nv]][nf])
+                imgname_new = join(outdir, '{:06d}.jpg'.format(nfnew))
+                copy_func(imgname_old, imgname_new)
+    save_json(join(out, 'match_name.json'), reports)
+
+from tabulate import tabulate
+def parse_time(imagelists, cams):
+    times_all = {}
+    headers = ['camera', 'frames', 'mean', 'min', 'max', 'number>mean', 'start', 'end']
+    MAX_STEP = 20
+    infos = []
+    start_time = -1
+    for cam in cams:
+        times = []
+        for imgname in imagelists[cam]:
+            time = parseImg(imgname)['time']
+            times.append(time)
+        times = np.array(times)
+        times_all[cam] = times
+        if start_time < 0:
+            start_time = times[0]
+        else:
+            start_time = min(start_time, times[0])
+    print('Start time: {}'.format(start_time))
+    for cam in cams:
+        times = times_all[cam]
+        times -= start_time
+        delta = times[1:] - times[:-1]
+        infos.append([cam, times.shape[0], 
+            delta.mean(),
+            '{}/{}'.format(delta.min(), delta.argmin()), 
+            '{}/{}'.format(delta.max(), delta.argmax()), 
+            (delta>delta.mean()).sum(), 
+            times[0]%60000,
+            times[-1]%60000])
+    print(tabulate(infos, headers=headers))
+    return times_all
+
+def soft_sync(path, out, multiple_thread = False):
+    os.makedirs(out, exist_ok=True)
+    # 获取图像名称
+    cams, imagelists = getFileDict(path)
+    if args.static:
+        # 静止场景，直接保存第一帧图像
+        matched = np.zeros((len(cams), 1), dtype=np.int)
+    elif args.nosync:
+        assert len(cams) == 1
+        times_all = parse_time(imagelists, cams)
+        matched = np.arange(0, len(imagelists[cams[0]])).reshape(1, -1)
+        # matched = np.arange((1, len(imagelists[cams[0]])), dtype=np.int)
+    else:
+        # 获取图像时间
+        times_all = parse_time(imagelists, cams)
+        matched, matched_time = sync_by_name(imagelists, times_all, cams)
+        matched = matched[:, ::args.step]
+        times_all = {key:val.tolist() for key, val in times_all.items()}
+        save_json(join(out, 'timestamp.json'), times_all)
+        np.savetxt(join(out, 'sync_time.txt'), matched_time-matched_time.min(), fmt='%10d')
+        # 保存图像
+    copy_with_match(path, out, matched, imagelists, cams, multiple_thread)
+
+def read_json(path):
+    with open(path) as f:
+        data = json.load(f)
+    return data
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser(usage='''
+    origin=/path/to/CoreView_xxx
+    data=/path/to/output/xxx
+    - convert data: python3 scripts/dataset/pre_lightstage.py ${origin} ${data} --mp --ref_min
+    - keep origin resolution: --keep2048
+    - only copy one frame: --static
+    - set the color adjustment: --color /path/to/color
+    - skip the check: --nocheck
+''')
+    parser.add_argument('path', type=str)
+    parser.add_argument('out', type=str)
+    parser.add_argument('--filter', type=str, nargs='+', default=[])
+    parser.add_argument('--num', type=int, default=1)
+    parser.add_argument('--ref', type=str, default=None)
+    parser.add_argument('--color', type=str, default=None)
+    parser.add_argument('--step', type=int, default=1)
+    parser.add_argument('--keep2048', action='store_true')
+    parser.add_argument('--static', action='store_true')
+    parser.add_argument('--ref_min', action='store_true')
+    parser.add_argument('--nosync', action='store_true')
+    parser.add_argument('--debug', action='store_true')
+    parser.add_argument("--mp", action='store_true')
+    parser.add_argument('--check', action='store_true')
+    parser.add_argument('--nocheck', action='store_true')
+    args = parser.parse_args()
+
+    # Reading color adjustment
+    if args.color is not None:
+        colors_params = {}
+        for sub in sorted(os.listdir(args.color)):
+            colors = read_json(join(args.color, sub, '000000.json'))
+            colors_params[sub] = np.array(colors['params'], dtype=np.float32)
+        print('Reading color adjustment from {}'.format(args.color))
+    else:
+        colors_params = None
+    cmd = 'python3 ' + ' '.join(sys.argv)
+    os.makedirs(args.out, exist_ok=True)
+    print(cmd, file=open(join(args.out, 'cmd.log'), 'w'))
+    filter_list = args.filter
+
+    if args.check:
+        timestamp = np.loadtxt(join(args.out, 'sync_time.txt'), dtype=np.int)
+        timestamp = timestamp[:, :10]
+        t = np.arange(timestamp.shape[1])
+        import matplotlib as mpl
+        mpl.use('TkAgg')
+        import matplotlib.pyplot as plt
+        for nv in range(timestamp.shape[0]):
+            plt.plot(t, timestamp[nv])
+        for nf in range(timestamp.shape[1]-1):
+            plt.plot([nf, nf+1], [timestamp[:, nf].mean(), timestamp[:, nf].mean()], c='k')
+            plt.plot([nf, nf+1], [timestamp[:, nf].min(), timestamp[:, nf].min()], c='r')
+            plt.plot([nf, nf+1], [timestamp[:, nf].max(), timestamp[:, nf].max()], c='g')
+        plt.show()
+        import ipdb; ipdb.set_trace()
+    else:
+        soft_sync(args.path, args.out, multiple_thread = args.mp)