diff --git a/README.md b/README.md
index ad357a0..e50ca0b 100644
--- a/README.md
+++ b/README.md
@@ -102,7 +102,7 @@ If you prefer to build the Docker image by yourself, Check [build-docker-image](
 
 ## 1. Data Preparation
 
-Refer to [dataprocess/README.md](dataprocess/README.md) for dataset download instructions. Currently, we support **Argoverse 2**, **Waymo**, **nuScenes**, **ZOD** and **custom datasets** (more datasets will be added in the future). 
+Refer to [dataprocess/README.md](dataprocess/README.md) for dataset download instructions. Currently, we support **Argoverse 2**, **Waymo**, **nuScenes**, [**MAN-TruckScene**](https://github.com/TUMFTM/truckscenes-devkit), [**ZOD**](https://github.com/zenseact/zod) and **custom datasets** (more datasets will be added in the future). 
 
 After downloading, convert the raw data to `.h5` format for easy training, evaluation, and visualization. Follow the steps in [dataprocess/README.md#process](dataprocess/README.md#process). 
 
@@ -337,6 +337,12 @@ If you find it useful, please cite our works:
     year={2025},
     journal={arXiv preprint arXiv:2503.00803},
 }
+@article{zhang2025deltaflow,
+    title={{DeltaFlow}: An Efficient Multi-frame Scene Flow Estimation Method},
+    author={Zhang, Qingwen and Zhu, Xiaomeng and Zhang, Yushan and Cai, Yixi and Andersson, Olov and Jensfelt, Patric},
+    year={2025},
+    journal={arXiv preprint arXiv:2508.17054},
+}
 ```
 
 And our excellent collaborators works contributed to this codebase also:
diff --git a/assets/slurm/0_process.sh b/assets/slurm/0_process.sh
deleted file mode 100644
index 44481f2..0000000
--- a/assets/slurm/0_process.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/bin/bash
-#SBATCH -J pack_data
-#SBATCH --gpus 0
-#SBATCH --cpus-per-task 64
-#SBATCH --mem 256G
-#SBATCH --mincpus=64
-#SBATCH -t 1-00:00:00
-#SBATCH --mail-type=END,FAIL
-#SBATCH --mail-user=qingwen@kth.se
-#SBATCH --output /proj/berzelius-2023-154/users/x_qinzh/workspace/OpenSceneFlow/logs/slurm/%J_data.out
-#SBATCH --error  /proj/berzelius-2023-154/users/x_qinzh/workspace/OpenSceneFlow/logs/slurm/%J_data.err
-
-PYTHON=/proj/berzelius-2023-154/users/x_qinzh/mambaforge/envs/sftool/bin/python
-export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/proj/berzelius-2023-154/users/x_qinzh/mambaforge/lib
-cd /proj/berzelius-2023-364/users/x_qinzh/workspace/OpenSceneFlow
-# export HYDRA_FULL_ERROR=1
-
-
-$PYTHON dataprocess/extract_av2.py --nproc 64 \
-    --av2_type sensor \
-    --data_mode train \
-    --argo_dir /proj/berzelius-2023-154/users/x_qinzh/av2 \
-    --output_dir /proj/berzelius-2023-364/users/x_qinzh/data/av2/h5py
-
-$PYTHON dataprocess/extract_av2.py --nproc 64 \
-    --av2_type sensor \
-    --data_mode val \
-    --argo_dir /proj/berzelius-2023-154/users/x_qinzh/av2 \
-    --output_dir /proj/berzelius-2023-364/users/x_qinzh/data/av2/h5py \
-    --mask_dir /proj/berzelius-2023-154/users/x_qinzh/av2/3d_scene_flow
-
-$PYTHON dataprocess/extract_av2.py --nproc 64 \
-    --av2_type sensor \
-    --data_mode test \
-    --argo_dir /proj/berzelius-2023-154/users/x_qinzh/av2 \
-    --output_dir /proj/berzelius-2023-364/users/x_qinzh/data/av2/h5py \
-    --mask_dir /proj/berzelius-2023-154/users/x_qinzh/av2/3d_scene_flow
\ No newline at end of file
diff --git a/assets/slurm/data-process.sh b/assets/slurm/data-process.sh
new file mode 100644
index 0000000..d132e91
--- /dev/null
+++ b/assets/slurm/data-process.sh
@@ -0,0 +1,44 @@
+#!/bin/bash
+#SBATCH -J dataprocess
+#SBATCH -p berzelius-cpu
+#SBATCH --cpus-per-task 64
+#SBATCH --mem 128G
+#SBATCH --mincpus=64
+#SBATCH -t 2-00:00:00
+#SBATCH --mail-type=END,FAIL
+#SBATCH --mail-user=qingwen@kth.se
+#SBATCH --output /proj/berzelius-2023-154/users/x_qinzh/OpenSceneFlow/logs/slurm/%J_data.out
+#SBATCH --error  /proj/berzelius-2023-154/users/x_qinzh/OpenSceneFlow/logs/slurm/%J_data.err
+
+PYTHON=/proj/berzelius-2023-154/users/x_qinzh/mambaforge/envs/sftool/bin/python
+export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/proj/berzelius-2023-154/users/x_qinzh/mambaforge/lib
+cd /proj/berzelius-2023-154/users/x_qinzh/OpenSceneFlow/
+# export HYDRA_FULL_ERROR=1
+
+
+# =============== Argoverse2
+# $PYTHON dataprocess/extract_av2.py --nproc 64 \
+#     --av2_type sensor \
+#     --data_mode train \
+#     --argo_dir /proj/berzelius-2023-154/users/x_qinzh/av2 \
+#     --output_dir /proj/berzelius-2023-364/users/x_qinzh/data/av2/h5py
+
+# $PYTHON dataprocess/extract_av2.py --nproc 64 \
+#     --av2_type sensor \
+#     --data_mode val \
+#     --argo_dir /proj/berzelius-2023-154/users/x_qinzh/av2 \
+#     --output_dir /proj/berzelius-2023-364/users/x_qinzh/data/av2/h5py \
+#     --mask_dir /proj/berzelius-2023-154/users/x_qinzh/av2/3d_scene_flow
+
+# $PYTHON dataprocess/extract_av2.py --nproc 64 \
+#     --av2_type sensor \
+#     --data_mode test \
+#     --argo_dir /proj/berzelius-2023-154/users/x_qinzh/av2 \
+#     --output_dir /proj/berzelius-2023-364/users/x_qinzh/data/av2/h5py \
+#     --mask_dir /proj/berzelius-2023-154/users/x_qinzh/av2/3d_scene_flow
+
+# =============== MAN-Truckscenes
+$PYTHON dataprocess/extract_truckscenes.py --nproc 12 \
+    --data_dir /proj/berzelius-2023-364/users/x_qinzh/data/truckscenes/raw_data/trainval/man-truckscenes \
+    --mode v1.0-trainval \
+    --output_dir /proj/berzelius-2023-364/data/truckscenes/h5py
\ No newline at end of file
diff --git a/assets/tests/hdbscan_speed.py b/assets/tests/hdbscan_speed.py
index f92580d..b839b2c 100644
--- a/assets/tests/hdbscan_speed.py
+++ b/assets/tests/hdbscan_speed.py
@@ -25,7 +25,7 @@
 
 def vis(pc0, labels, title='HDBSCAN'):
     # visualize
-    vis = MyVisualizer(view_file=f'{BASEF_DIR}/assets/view/av2.json', window_title=title)
+    vis = MyVisualizer(view_file=f'{BASEF_DIR}/assets/view/demo.json', window_title=title)
     pcd = o3d.geometry.PointCloud()
     num_points = pc0.shape[0]
     pcd.points = o3d.utility.Vector3dVector(pc0)
diff --git a/assets/view/av2.json b/assets/view/av2.json
deleted file mode 100644
index b1bf1bb..0000000
--- a/assets/view/av2.json
+++ /dev/null
@@ -1,19 +0,0 @@
-{
-	"class_name" : "ViewTrajectory",
-	"interval" : 29,
-	"is_loop" : false,
-	"trajectory" : 
-	[
-		{
-			"boundingbox_max" : [ 211.125, 117.1875, 20.53125 ],
-			"boundingbox_min" : [ -215.25, -166.625, -3.392578125 ],
-			"field_of_view" : 90.0,
-			"front" : [ -0.78264077936294429, -0.0063155949007044129, 0.62244158259166249 ],
-			"lookat" : [ 16.764474584958553, 0.042979235705968843, -6.3527807404873249 ],
-			"up" : [ 0.62244710369604039, 0.0012897206826678895, 0.78266080757948497 ],
-			"zoom" : 0.080000000000000002
-		}
-	],
-	"version_major" : 1,
-	"version_minor" : 0
-}
\ No newline at end of file
diff --git a/assets/view/demo.json b/assets/view/demo.json
new file mode 100644
index 0000000..ddead1e
--- /dev/null
+++ b/assets/view/demo.json
@@ -0,0 +1,19 @@
+{
+	"class_name" : "ViewTrajectory",
+	"interval" : 29,
+	"is_loop" : false,
+	"trajectory" : 
+	[
+		{
+			"boundingbox_max" : [ 215.05035400390625, 80.301795959472656, 34.890830993652344 ],
+			"boundingbox_min" : [ -211.76950073242188, -125.19444274902344, -4.2829980850219727 ],
+			"field_of_view" : 90.0,
+			"front" : [ -0.58999680455306347, -0.15524870408634167, 0.79233932787454509 ],
+			"lookat" : [ 9.9369846496341676, 13.322769196068615, -16.164870152242376 ],
+			"up" : [ 0.79499677526385637, 0.059685607254847638, 0.60367023746967163 ],
+			"zoom" : 0.17999999999999999
+		}
+	],
+	"version_major" : 1,
+	"version_minor" : 0
+}
\ No newline at end of file
diff --git a/conf/others/truckscenes.toml b/conf/others/truckscenes.toml
new file mode 100644
index 0000000..9ebb7e1
--- /dev/null
+++ b/conf/others/truckscenes.toml
@@ -0,0 +1,31 @@
+
+[important]
+height = 0        # ego height. since truckscene have multiple LiDARs, we set pc to ego-coord already.
+
+# 整个雷点点云以自己为中心 分为多少个segment，每个segment又分成多少个bin
+[segments]
+r_min = 0.1          # minimum point distance. 感兴趣的区域
+r_max = 100           # maximum point distance.
+n_segments = 360     # number of radial segments.
+n_bins = 160         # number of radial bins.
+
+
+[ground]
+# 直线的 slope 斜率，一个seg里拟合的线斜率
+min_slope = 0.0      # minimum slope of a ground line. [T_m_small]
+max_slope = 0.1     # maximum slope of a ground line. [T_m]
+# 如果拟合点超过这个 long threshold，判断其 height 变化是否超过 max_long_height
+long_threshold = 2.0    # Distance at which points are considered far from each other. [T_d_prev]
+max_long_height = 0.3   # maximum height change to previous point in long line. [T_b]
+# 如果没有超过 long threshold，判断现在拟合的点与fitline高度变化是否超过 max_start_height
+max_start_height = 0.2  # Maximum heigh of starting line to be labelled ground.
+# 最大的拟合误差，超过这个误差的点不会被加入 line 拟合
+max_fit_error = 0.1  # maximum error of a point during line fit. [T_RMSE: will sqaure inside code.]
+
+# 判断是否是地面点的时候，需要满足的条件：
+max_dist_to_line = 0.2   # maximum vertical distance of point to line to be considered ground. [T_d_ground]
+line_search_angle = 2.0 # How far to search for a line in angular direction [rad].
+
+[general]
+n_threads = 8        # number of threads for parallel processing.
+verbose = false       # if you don't want to see every output, set this to false.
\ No newline at end of file
diff --git a/conf/ground/zod.toml b/conf/others/zod.toml
similarity index 93%
rename from conf/ground/zod.toml
rename to conf/others/zod.toml
index ab5e85b..3dfdcc4 100644
--- a/conf/ground/zod.toml
+++ b/conf/others/zod.toml
@@ -23,7 +23,7 @@ max_start_height = 0.2  # Maximum heigh of starting line to be labelled ground.
 max_fit_error = 0.1  # maximum error of a point during line fit. [T_RMSE: will sqaure inside code.]
 
 # 判断是否是地面点的时候，需要满足的条件：
-max_dist_to_line = 0.2   # maximum vertical distance of point to line to be considered ground. [T_d_ground]
+max_dist_to_line = 0.25   # maximum vertical distance of point to line to be considered ground. [T_d_ground]
 line_search_angle = 2.0 # How far to search for a line in angular direction [rad].
 
 [general]
diff --git a/dataprocess/README.md b/dataprocess/README.md
index 2651afa..419098b 100644
--- a/dataprocess/README.md
+++ b/dataprocess/README.md
@@ -6,13 +6,13 @@ README for downloading and preprocessing the dataset. We includes waymo, argover
 - [Download](#download): includes how to download the dataset.
 - [Process](#process): run script to preprocess the dataset.
 
-We've updated the process dataset for:
+We've updated the process dataset for (Please cite the original dataset paper and involved work if you use them):
 
 - [x] Argoverse 2.0: check [here](#argoverse-20). The process script Involved from [DeFlow](https://github.com/KTH-RPL/DeFlow).
 - [x] Waymo: check [here](#waymo-dataset). The process script was involved from [SeFlow](https://github.com/KTH-RPL/SeFlow).
 - [x] nuScenes: check [here](#nuscenes), The process script was involved from [DeltaFlow](https://github.com/Kin-Zhang/DeltaFlow).
 - [x] ZOD (w/o gt): check [here](#zod-dataset). The process script was involved from [HiMo](https://kin-zhang.github.io/HiMo). (It could be a good first reference for users to extract other datasets in the future.)
-- [ ] TruckScene: done coding, public after review. Will be involved later by another paper.
+- [x] TruckScene: check [here](#truckscene). The process script was involved from [DoGFlow](https://github.com/ajinkyakhoche/DoGFlow).
 
 If you want to **use all datasets above**, there is a **specific environment** in [envsftool.yaml](../envsftool.yaml) to install all the necessary packages. As Waymo package have different configuration and conflict with the main environment. Setup through the following command:
 
@@ -134,6 +134,46 @@ For HiMo, we only downloaded [drives-set](https://zod.zenseact.com/drives/) for
 
 Please check the scripts: [dataprocess/extract_zod.py](./extract_zod.py) in detail, current we only process one scene while feel free to comment out for all scene etc.
 
+### TruckScene
+
+Please visit the [TruckScene dataset](https://brandportal.man/d/QSf8mPdU5Hgj/downloads#/-/dataset) page for privacy policy. You can download the dataset by following command:
+
+```bash
+# mini set, ~11G recommended for debugging purpose
+cd /home/kin/data/truckscene/mini
+wget https://man-truckscenes.s3.eu-central-1.amazonaws.com/release/mini/man-truckscenes_metadata_v1.0-mini.zip
+wget https://man-truckscenes.s3.eu-central-1.amazonaws.com/release/mini/man-truckscenes_sensordata_v1.0-mini.zip
+unzip "man-truckscenes_*.zip"
+
+# full trainval set, ~630G
+cd /home/kin/data/truckscene/trainval
+wget https://man-truckscenes.s3.eu-central-1.amazonaws.com/release/trainval/man-truckscenes_metadata_v1.0-trainval.zip
+wget https://man-truckscenes.s3.eu-central-1.amazonaws.com/release/trainval/man-truckscenes_sensordata01_v1.0-trainval.zip
+wget https://man-truckscenes.s3.eu-central-1.amazonaws.com/release/trainval/man-truckscenes_sensordata02_v1.0-trainval.zip
+wget https://man-truckscenes.s3.eu-central-1.amazonaws.com/release/trainval/man-truckscenes_sensordata03_v1.0-trainval.zip
+wget https://man-truckscenes.s3.eu-central-1.amazonaws.com/release/trainval/man-truckscenes_sensordata04_v1.0-trainval.zip
+wget https://man-truckscenes.s3.eu-central-1.amazonaws.com/release/trainval/man-truckscenes_sensordata05_v1.0-trainval.zip
+wget https://man-truckscenes.s3.eu-central-1.amazonaws.com/release/trainval/man-truckscenes_sensordata06_v1.0-trainval.zip
+wget https://man-truckscenes.s3.eu-central-1.amazonaws.com/release/trainval/man-truckscenes_sensordata07_v1.0-trainval.zip
+unzip "man-truckscenes_*.zip"
+```
+
+Folder structure:
+```
+truckscene/
+  — samples/
+  — sweeps/
+  — v1.0-mini/
+  — v1.0-trainval/
+```
+
+#### Dataset frames
+
+| Dataset | # Total Scene | # Total Frames |
+| ------- | ------------- | -------------- |
+| train   | 524           | 101902 / 20380 (w. gt)         |
+| val     | 75           | 14625 / 2925 (w. gt)          |
+
 
 
 ## Process
@@ -144,8 +184,9 @@ This directory contains the scripts to preprocess the datasets into `.h5` files.
 - `extract_nus.py`: Process the datasets in nuScenes.
 - `extract_waymo.py`: Process the datasets in Waymo.
 - `extract_zod.py`: Process the datasets in ZOD.
+- `extract_truckscene.py`: Process the datasets in TruckScene.
 
-Example Running command:
+Example Running command, you can also check our [slurm data-process script](../assets/slurm/data-process.sh) for more details.:
 ```bash
 # av2:
 python dataprocess/extract_av2.py --av2_type sensor --data_mode train --argo_dir /home/kin/data/av2 --output_dir /home/kin/data/av2/h5py
@@ -155,6 +196,9 @@ python dataprocess/extract_waymo.py --mode train --flow_data_dir /home/kin/data/
 
 # nus:
 python dataprocess/extract_nus.py --mode v1.0-trainval --output_dir /home/kin/data/nus/h5py/full --nproc 24
+
+# truckscene:
+python dataprocess/extract_truckscene.py --data_dir /home/kin/data/man-truckscenes --mode v1.0-mini --output_dir /home/kin/data/nus/h5py
 ```
 
 
diff --git a/dataprocess/extract_nus.py b/dataprocess/extract_nus.py
index 4fcd2ad..1718176 100644
--- a/dataprocess/extract_nus.py
+++ b/dataprocess/extract_nus.py
@@ -18,7 +18,7 @@
 import multiprocessing
 from pathlib import Path
 from multiprocessing import Pool, current_process
-from typing import Optional, Final
+from typing import Optional
 from tqdm import tqdm
 import numpy as np
 import fire, time, h5py
@@ -69,10 +69,13 @@ def _load_points_from_file(filename: str) -> np.ndarray:
     pc = pc.reshape((-1, 5))[:, :4]
     return pc
         
-def get_pose(nusc, sweep_data):
-    # pose from lidar to world
-    ego2lidar = nusc.get('calibrated_sensor', sweep_data['calibrated_sensor_token'])
-    world2ego = nusc.get('ego_pose', sweep_data['ego_pose_token'])
+def get_pose(data_fn, sweep_data, w2stf=True):
+    world2ego = data_fn.get('ego_pose', sweep_data['ego_pose_token'])
+    # without considering the sensor to ego transform, we will do it outside if multiple sensors are used.
+    if not w2stf:
+        return transform_matrix(world2ego['translation'], Quaternion(world2ego['rotation'])).astype(np.float64)
+
+    ego2lidar = data_fn.get('calibrated_sensor', sweep_data['calibrated_sensor_token'])
     ego2lidar_np = transform_matrix(ego2lidar['translation'], Quaternion(ego2lidar['rotation']))
     world2ego_np = transform_matrix(world2ego['translation'], Quaternion(world2ego['rotation']))
     return np.dot(world2ego_np, ego2lidar_np)
@@ -85,13 +88,13 @@ def if_annotated_frame(sample_ann_dict, ts0):
             break
     return gt_flow_flag
 
-def _resample_data(nusc, sample_data, sample_ann_dict, resample2frequency=10):
+def _resample_data(nusc, sample_data, sample_ann_dict, datafrequency=20, resample2frequency=10):
     """
     NOTE(Qingwen) - 2025-05-18:
     We always want to start from the first GT frame, and then resample the data! So we have as many GT frames as possible...
     """
     sweep_data_lst, timestamps_lst = [], []
-    skipFrame = int(20 / resample2frequency)  # since nuscenes sweep at 20Hz, we want to resample to 10Hz
+    skipFrame = int(datafrequency / resample2frequency)  # since nuscenes sweep at 20Hz, we want to resample to 10Hz
     cnt = 0
 
     # Find the first GT frame
@@ -268,18 +271,17 @@ def proc(x, ignore_current_process=False):
         pos = 1
     process_log(*x, n=pos)
     
-def process_logs(nusc_mode, data_dir: Path, scene_list: list, output_dir: Path, nproc: int):
-    """Compute sceneflow for all logs in the dataset. Logs are processed in parallel.
-       Args:
-         data_dir: Argoverse 2.0 directory
-         output_dir: Output directory.
+def process_logs(data_mode, data_dir: Path, scene_list: list, output_dir: Path, nproc: int):
     """
-    
+    Compute sceneflow for all logs in the dataset. 
+    Logs are processed in parallel.
+    """
+
     if not (data_dir).exists():
         print(f'{data_dir} not found')
         return
 
-    args = sorted([(nusc_mode, data_dir, scene_num_id, output_dir) for scene_num_id in range(len(scene_list))])
+    args = sorted([(data_mode, data_dir, scene_num_id, output_dir) for scene_num_id in range(len(scene_list))])
     print(f'Using {nproc} processes')
     
     # # for debug
@@ -295,7 +297,7 @@ def process_logs(nusc_mode, data_dir: Path, scene_list: list, output_dir: Path,
             res = list(tqdm(p.imap_unordered(proc, args), total=len(scene_list), ncols=100))
 
 def main(
-    data_dir: str = "/home/kin/data/nus/v1.0",
+    data_dir: str = "/home/kin/data/nus/raw",
     mode: str = "v1.0-mini",
     output_dir: str ="/home/kin/data/nus/h5py/demo",
     nproc: int = (multiprocessing.cpu_count() - 1),
diff --git a/dataprocess/extract_truckscenes.py b/dataprocess/extract_truckscenes.py
new file mode 100644
index 0000000..dcc3008
--- /dev/null
+++ b/dataprocess/extract_truckscenes.py
@@ -0,0 +1,294 @@
+"""
+# 
+# Created: 2025-09-06 09:16
+# Copyright (C) 2025-now, RPL, KTH Royal Institute of Technology
+# Author: Qingwen Zhang (https://kin-zhang.github.io/), Ajinkya Khoche (https://ajinkyakhoche.github.io/)
+#
+# Description: Preprocess Data, save as h5df format for faster loading
+# This one is for MAN TruckScenes dataset
+# 
+# NOTE: truckscene follow really similar structure with TruckScenes format. That's why
+# **NOT ALL** frames are annotated! So the SL training might be not that effective.
+# Since truckscenes LiDAR is at 10Hz, we keep annotated flow to 10Hz also.
+#
+"""
+
+from collections import defaultdict
+import os
+# os.environ["OMP_NUM_THREADS"] = "1"
+
+from pathlib import Path
+import multiprocessing
+from multiprocessing import Pool, current_process
+from typing import Optional
+from tqdm import tqdm
+import numpy as np
+import fire, time, h5py
+
+from truckscenes import TruckScenes
+from truckscenes.utils import splits
+from truckscenes.utils.geometry_utils import transform_matrix
+from truckscenes.utils.geometry_utils import points_in_box
+from truckscenes.utils.data_classes import LidarPointCloud
+from pyquaternion import Quaternion
+
+import os, sys
+PARENT_DIR = os.path.abspath(os.path.join( os.path.dirname( __file__ ), '..'))
+sys.path.append(PARENT_DIR)
+from dataprocess.misc_data import create_reading_index, check_h5py_file_exists, ManNamMap, find_closest_integer_in_ref_arr
+from dataprocess.extract_nus import remove_ego_points, get_pose
+from src.utils import npcal_pose0to1
+from src.utils.av2_eval import CATEGORY_TO_INDEX
+from linefit import ground_seg
+
+
+GROUNDSEG_config = f"{PARENT_DIR}/conf/others/truckscenes.toml"
+# NOTE(Qingwen): we only select 2x64 long-range LiDARs for processing, you can add more sensors if needed
+# The timestamp reference will always be the last item in this list
+SelectedSensor = ['LIDAR_RIGHT', 'LIDAR_LEFT'] # 2x64
+# SelectedSensor = ['LIDAR_TOP_FRONT', 'LIDAR_TOP_LEFT', 'LIDAR_TOP_RIGHT', 'LIDAR_REAR'] # 4x32
+# SelectedSensor = ['LIDAR_LEFT', 'LIDAR_RIGHT', 'LIDAR_TOP_FRONT', 'LIDAR_TOP_LEFT', 'LIDAR_TOP_RIGHT', 'LIDAR_REAR'] # all 6 LiDARs
+
+def process_log(data_mode, data_dir: Path, scene_num_id: int, output_dir: Path, n: Optional[int] = None) :
+    def create_group_data(group, pc, pose, lidar_id, lidar_center, gm = None, flow_0to1=None, flow_valid=None, flow_category=None, flow_instance=None, ego_motion=None):
+        group.create_dataset('lidar', data=pc.astype(np.float32))
+        group.create_dataset('pose', data=pose.astype(np.float64))
+        group.create_dataset('ground_mask', data=gm.astype(bool))
+        group.create_dataset('lidar_id', data=lidar_id.astype(np.float32))
+        group.create_dataset('lidar_center', data=lidar_center.astype(np.float32)) # shape: [LiDAR_num, 3] (x, y, z)
+        if ego_motion is not None:
+            group.create_dataset('ego_motion', data=ego_motion.astype(np.float32))
+        if flow_0to1 is not None:
+            # ground truth flow information
+            group.create_dataset('flow', data=flow_0to1.astype(np.float32))
+            group.create_dataset('flow_is_valid', data=flow_valid.astype(bool))
+            group.create_dataset('flow_category_indices', data=flow_category.astype(np.uint8))
+            group.create_dataset('flow_instance_id', data=flow_instance.astype(np.int16))
+
+    def compute_flow_simple(data_fn, pc0, pose0, pose1, ts0, ts1, sample_ann_list, dclass, DataNameMap=ManNamMap):
+        # compute delta transform between pose0 and pose1
+        ego1_SE3_ego0 = npcal_pose0to1(pose0, pose1)
+        # flow due to ego motion
+        flow = np.zeros_like(pc0[:,:3])
+        flow = pc0[:,:3] @ ego1_SE3_ego0[:3,:3].T + ego1_SE3_ego0[:3,3] - pc0[:,:3] # pose flow
+
+        valid = np.ones(len(pc0), dtype=np.bool_)
+        classes = np.zeros(len(pc0), dtype=np.uint8)
+        instances = np.zeros(len(pc0), dtype=np.int16)
+
+        delta_t = (ts1 - ts0) * 1e-6
+        world_pc0 = pc0[:,:3] @ pose0[:3,:3].T + pose0[:3,3]
+        id_ = 0
+        for ann in sample_ann_list:
+            # ann_vel = ann.velocity  # it's nan in truckscenes, we need to compute... don't know why
+            ann_vel = data_fn.box_velocity(ann.token) # in world frame
+            if np.isnan(ann_vel).any():
+                continue
+
+            # previous x/y/z velocity is on world frame, need to transform to ego frame
+            ann_vel = ann_vel @ pose0[:3,:3]
+            cls = ann.name
+
+            # extend box length according to velocity, ref HiMo.
+            ann.wlh[1] = ann.wlh[1] + (np.linalg.norm(ann_vel) * delta_t / 2)
+            ann.wlh[2] = ann.wlh[2] + 0.2 # some truck top missing points, extend a bit
+            points_in_box_mask = points_in_box(ann, world_pc0[:,:3].T, wlh_factor=1.1)
+            classes[points_in_box_mask] = CATEGORY_TO_INDEX[DataNameMap[cls]]
+
+            if np.sum(points_in_box_mask) > 5:
+                obj_flow = np.ones_like(pc0[points_in_box_mask,:3]) * ann_vel * delta_t
+                flow[points_in_box_mask] += obj_flow
+                instances[points_in_box_mask] = (dclass[id_]+1)
+                id_ += 1
+            else:
+                valid[points_in_box_mask] = False
+
+        return {'flow_0_1': flow, 'valid_0': valid, 'classes_0': classes, 
+                'ego_motion': ego1_SE3_ego0, 'flow_instance_id': instances}
+    
+    mants = TruckScenes(dataroot=data_dir, version=data_mode, verbose=False)
+    scene = mants.scene[scene_num_id]
+    log_id = scene['name']
+    
+    # In man-truckscenes, samples are annotated at 2 Hz and sweeps at 10 Hz. 
+    sample_data_lst = []
+    for sample in mants.sample:
+        if sample['scene_token'] != scene['token']:
+            continue
+        else:
+            sample_data_lst.append(sample)
+
+    now_sample_token_str = scene['first_sample_token']
+    sample = mants.get('sample', now_sample_token_str)
+
+    # initialize full sweep data dict
+    full_sweep_data_dict, timestamps = {}, {}
+    for sensor_name in SelectedSensor:
+        full_sweep_data_dict[sensor_name] = []
+        timestamps[sensor_name] = []
+
+    for channel, token in sample['data'].items():
+        if channel in SelectedSensor:
+            sample_data = mants.get('sample_data', token)
+            while sample_data['next'] != '':
+                ts0 = sample_data['timestamp']
+                full_sweep_data_dict[channel].append(sample_data)
+                timestamps[channel].append(ts0)
+                sample_data = mants.get('sample_data', sample_data['next'])
+
+    if check_h5py_file_exists(output_dir/f'{log_id}.h5', timestamps[SelectedSensor[-1]]):
+        print(f'{log_id} already exists and all timestamps are , skip...')
+        return            
+
+    dclass = defaultdict(lambda: len(dclass))
+    mygroundseg = ground_seg(GROUNDSEG_config)
+
+
+    with h5py.File(output_dir/f'{log_id}.h5', 'a') as f:
+        for cnt, sweep_data in enumerate(full_sweep_data_dict[SelectedSensor[-1]]):
+            ts0 = sweep_data['timestamp']
+            pose0 = get_pose(mants, sweep_data, w2stf=False)
+
+            lidar_list, lidar_center, lidar_dt, lidar_id = [], [], [], []; lidar_id_cnt = 0
+            for single_sensor_name in full_sweep_data_dict.keys():
+                # load closest sensor point cloud in reference ego frame
+                sensor_sweep_list = full_sweep_data_dict[single_sensor_name]
+                closest_ch_ind, closest_ch_timestamp, timestamp_diff = find_closest_integer_in_ref_arr(
+                    ts0, np.array([t['timestamp'] for t in sensor_sweep_list])
+                )
+                sensor_sweep = sensor_sweep_list[closest_ch_ind]
+
+                ego2lidar = mants.get('calibrated_sensor', sensor_sweep['calibrated_sensor_token'])
+                ego2lidar_np = transform_matrix(ego2lidar['translation'], Quaternion(ego2lidar['rotation']))
+
+                if 'LIDAR' in single_sensor_name:
+                    pc = LidarPointCloud.from_file(os.path.join(str(data_dir), sensor_sweep['filename'])).points.T 
+                    # NOTE(Qingwen): we need all points in base_link (ego) coordinate, but we save sensor center also
+                    pc[:,:3] = pc[:,:3] @ ego2lidar_np[:3,:3].T + ego2lidar_np[:3,3].T
+                    lidar_list.append(pc)
+                    
+                    # lidar_dt.append(np.ones(pc.shape[0]) * timestamp_diff * 1e-6) # microsecond to s
+                    lidar_id.append(np.ones(pc.shape[0]) * lidar_id_cnt)
+                    # lidar_center.append(ego2lidar_np[:3,3].T) # x, y, z
+                    lidar_center.append(ego2lidar_np)
+                    lidar_id_cnt += 1
+
+            points = np.vstack(lidar_list)
+            # lidar_dt = np.hstack(lidar_dt)
+            lidar_id = np.hstack(lidar_id)
+            lidar_center = np.array(lidar_center) # shape Num_LiDAR x 4x4
+
+            points = remove_ego_points(points, length_threshold=2.0, width_threshold=7.0)
+            is_ground_0 = np.array(mygroundseg.run(points[:, :3]))
+
+            if cnt == len(full_sweep_data_dict[SelectedSensor[-1]]) - 1:
+                group = f.create_group(str(ts0))
+                create_group_data(group=group, pc=points, gm=is_ground_0.astype(np.bool_), pose=pose0, \
+                                lidar_id=lidar_id, lidar_center=lidar_center)
+            else:
+                sweep_data_next = full_sweep_data_dict[SelectedSensor[-1]][cnt+1]
+                ts1 = sweep_data_next['timestamp']
+                pose1 = get_pose(mants, sweep_data_next, w2stf=False)
+                
+                group = f.create_group(str(ts0))
+                # annotated frame, compute flow
+                if sweep_data['is_key_frame'] and sweep_data['prev'] != "":
+                    curr_scene_ann = mants.get_boxes(sweep_data['token'])
+                    scene_flow = compute_flow_simple(mants, points, pose0, pose1, ts0, ts1, curr_scene_ann, dclass, DataNameMap=ManNamMap)
+
+                    create_group_data(group=group, pc=points, gm=is_ground_0.astype(np.bool_), pose=pose0, \
+                                    lidar_id=lidar_id, lidar_center=lidar_center, \
+                                    flow_0to1=scene_flow['flow_0_1'], flow_valid=scene_flow['valid_0'], flow_category=scene_flow['classes_0'], \
+                                    flow_instance=scene_flow['flow_instance_id'],
+                                    ego_motion=scene_flow['ego_motion'])
+                else: # no annotations, only save data
+                    create_group_data(group=group, pc=points, gm=is_ground_0.astype(np.bool_), pose=pose0, \
+                                        lidar_id=lidar_id, lidar_center=lidar_center, \
+                                        ego_motion=npcal_pose0to1(pose0, pose1))
+
+def proc(x, ignore_current_process=False):
+    if not ignore_current_process:
+        current=current_process()
+        pos = current._identity[0]
+    else:
+        pos = 1
+    process_log(*x, n=pos)
+    
+def process_logs(data_mode, data_dir: Path, scene_list: list, output_dir: Path, nproc: int):
+    """
+    Compute sceneflow for all logs in the dataset. 
+    Logs are processed in parallel.
+    """
+    
+    if not (data_dir).exists():
+        print(f'{data_dir} not found')
+        return
+
+    args = sorted([(data_mode, data_dir, scene_num_id, output_dir) for scene_num_id in range(len(scene_list))])
+    print(f'Using {nproc} processes')
+    
+    # # for debug
+    # for x in tqdm(args[:2]):
+    #     proc(x, ignore_current_process=True)
+    #     # break
+
+    if nproc <= 1:
+        for x in tqdm(args):
+            proc(x, ignore_current_process=True)
+    else:
+        with Pool(processes=nproc) as p:
+            res = list(tqdm(p.imap_unordered(proc, args), total=len(scene_list), ncols=100))
+
+def main(
+    data_dir: str = "/home/kin/data/truckscenes/man-truckscenes",
+    mode: str = "v1.0-mini",
+    output_dir: str ="/home/kin/data/truckscenes/h5py",
+    nproc: int = (multiprocessing.cpu_count() - 1),
+    only_index: bool = False,
+    split_name = None
+):
+    available_vers = ['v1.0-trainval', 'v1.0-test', 'v1.0-mini'] # defined by nus.
+    assert mode in available_vers
+    # man = TruckScenes(dataroot=data_dir, version=mode, verbose=False)
+    # print(f"Processing {mode} dataset with {len(man.scene)} scenes...")
+
+    if mode == 'v1.0-trainval':
+        train_scenes = splits.train
+        val_scenes = splits.val
+        if split_name is not None and split_name == 'train':
+            input_dict = {'train': train_scenes}
+        elif split_name is not None and split_name == 'val':
+            input_dict = {'val': val_scenes}
+        else:
+            input_dict = {'train': train_scenes, 'val': val_scenes}
+    elif mode == 'v1.0-test':
+        test_scenes = splits.test
+        input_dict = {'test': test_scenes}
+    elif mode == 'v1.0-mini':
+        train_scenes = splits.mini_train
+        val_scenes = splits.mini_val
+        input_dict = {
+            'train': train_scenes,
+            'val': val_scenes
+        }
+        # NOTE(Qingwen): or if you don't want to split mini, use below
+        # input_dict = {'mini': train_scenes + val_scenes}
+    else:
+        raise ValueError('unknown')
+
+    for input_key, input_val in input_dict.items():
+        output_dir_ = Path(output_dir) / input_key
+        print("[INFO] We are processing data to ", output_dir_)
+        if only_index:
+            create_reading_index(Path(output_dir_))
+            create_reading_index(Path(output_dir_), flow_inside_check=True)
+            return
+        output_dir_.mkdir(exist_ok=True, parents=True)
+        process_logs(mode, Path(data_dir), input_val, output_dir_, nproc)
+        create_reading_index(output_dir_)
+        create_reading_index(Path(output_dir_), flow_inside_check=True)
+
+if __name__ == '__main__':
+    start_time = time.time()
+    fire.Fire(main)
+    print(f"\nTime used: {(time.time() - start_time)/60:.2f} mins")
\ No newline at end of file
diff --git a/dataprocess/extract_zod.py b/dataprocess/extract_zod.py
index d60ecdd..295c0c7 100644
--- a/dataprocess/extract_zod.py
+++ b/dataprocess/extract_zod.py
@@ -25,7 +25,7 @@
 from dataprocess.misc_data import create_reading_index
 
 from linefit import ground_seg
-GROUNDSEG_config = f"{BASE_DIR}/conf/ground/zod.toml"
+GROUNDSEG_config = f"{BASE_DIR}/conf/others/zod.toml"
 
 def process_log(data_dir: Path, log_id: str, output_dir: Path, n: Optional[int] = None) :
 
diff --git a/dataprocess/misc_data.py b/dataprocess/misc_data.py
index b749a3c..b292941 100644
--- a/dataprocess/misc_data.py
+++ b/dataprocess/misc_data.py
@@ -3,6 +3,10 @@
 from pathlib import Path
 from tqdm import tqdm
 
+from typing import Tuple, cast
+
+NDArrayInt = np.typing.NDArray[np.int64]
+
 def check_h5py_file_exists(h5py_file: Path, timestamps: list, verbose: bool = False) -> bool:
     if not h5py_file.exists():
         return False
@@ -52,6 +56,32 @@ def create_reading_index(data_dir: Path, flow_inside_check=False):
         pickle.dump(data_index, f)
         print(f"Create {pkl_file_name} index Successfully, cost: {time.time() - start_time:.2f} s")
 
+def find_closest_integer_in_ref_arr(
+    query_int: int, ref_arr: NDArrayInt
+) -> Tuple[int, int, int]:
+    """Find the closest integer to any integer inside a reference array, and the corresponding difference.
+
+    In our use case, the query integer represents a nanosecond-discretized timestamp, and the
+    reference array represents a numpy array of nanosecond-discretized timestamps.
+
+    Instead of sorting the whole array of timestamp differences, we just
+    take the minimum value (to speed up this function).
+
+    Args:
+        query_int: query integer,
+        ref_arr: Numpy array of integers
+
+    Returns:
+        integer, representing the closest integer found in a reference array to a query
+        integer, representing the integer difference between the match and query integers
+    """
+    closest_ind = np.argmin(np.absolute(ref_arr - query_int))
+    closest_int = cast(
+        int, ref_arr[closest_ind]
+    )  # mypy does not understand numpy arrays
+    int_diff = np.absolute(query_int - closest_int)
+    return closest_ind, closest_int, int_diff
+
 class SE2:
 
     def __init__(self, rotation: np.ndarray, translation: np.ndarray) -> None:
@@ -155,4 +185,35 @@ def compose(self, right_se2: "SE2") -> "SE2":
     'static.other': 'NONE',
     'static.vegetation': 'NONE',
     'vehicle.ego': 'NONE'
+}
+
+## ====> MAN to Argoverse Mapping
+ManNamMap = {
+    "animal": 'NONE',
+    "human.pedestrian.adult": 'PEDESTRIAN',
+    "human.pedestrian.child": 'PEDESTRIAN',
+    "human.pedestrian.construction_worker": 'PEDESTRIAN',
+    "human.pedestrian.personal_mobility": 'PEDESTRIAN',
+    "human.pedestrian.police_officer": 'PEDESTRIAN',
+    "human.pedestrian.stroller": 'STROLLER',
+    "human.pedestrian.wheelchair": 'WHEELCHAIR',
+    "movable_object.barrier": 'NONE',
+    "movable_object.debris": 'NONE',
+    "movable_object.pushable_pullable": 'NONE',
+    "movable_object.trafficcone": 'CONSTRUCTION_CONE',
+    "static_object.bicycle_rack": 'NONE',
+    "static_object.traffic_sign": 'SIGN',
+    "vehicle.bicycle": 'BICYCLE',
+    "vehicle.bus.bendy": 'ARTICULATED_BUS',
+    "vehicle.bus.rigid": 'BUS',
+    "vehicle.car": 'REGULAR_VEHICLE',
+    "vehicle.construction": 'LARGE_VEHICLE',
+    "vehicle.emergency.ambulance": 'LARGE_VEHICLE',
+    "vehicle.emergency.police": 'REGULAR_VEHICLE',
+    "vehicle.motorcycle": 'MOTORCYCLE',
+    "vehicle.trailer": 'VEHICULAR_TRAILER',
+    "vehicle.truck": 'TRUCK',
+    "vehicle.train": 'NONE',
+    "vehicle.other": 'NONE',
+    "vehicle.ego_trailer": 'NONE',
 }
\ No newline at end of file
diff --git a/envsftool.yaml b/envsftool.yaml
index 887457a..4dc1f09 100644
--- a/envsftool.yaml
+++ b/envsftool.yaml
@@ -24,7 +24,7 @@ dependencies:
     - av2==0.2.1
     - waymo-open-dataset-tf-2.11.0==1.5.0
     - zod==0.5.0
-    - truckscenes-devkit
+    - truckscenes-devkit==1.0.0
     # Qingwen's public pkg:
     - dztimer
     - dufomap==1.1.0
diff --git a/tools/visualization.py b/tools/visualization.py
index e1d5e41..67a3e43 100644
--- a/tools/visualization.py
+++ b/tools/visualization.py
@@ -30,7 +30,7 @@
 from src.utils.o3d_view import MyVisualizer, MyMultiVisualizer, color_map, create_bev_square
 
 
-VIEW_FILE = f"{BASE_DIR}/assets/view/av2.json"
+VIEW_FILE = f"{BASE_DIR}/assets/view/demo.json"
 
 def check_flow(
     data_dir: str ="/home/kin/data/av2/preprocess/sensor/mini",