From 0c5b8519399297071d6d69f094a883aa3ae46b1d Mon Sep 17 00:00:00 2001 From: MaxChanger Date: Tue, 5 Jul 2022 23:37:14 +0800 Subject: [PATCH] update --- README.md | 35 ++++++++++++++------- assets/visualization.jpg | Bin 0 -> 856295 bytes config/kitti_road_mos.md | 7 +++-- utils/download_kitti_road.sh | 58 +++++++++++++++++++++++++++++++++++ 4 files changed, 87 insertions(+), 13 deletions(-) create mode 100644 assets/visualization.jpg create mode 100644 utils/download_kitti_road.sh diff --git a/README.md b/README.md index f3465fd..f1d4be0 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,20 @@ # Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation + +### [Project page](https://npucvr.github.io/MotionSeg3D)
+ This repo contains the code for our paper: > Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation. > [Jiadai Sun](https://github.com/MaxChanger), [Yuchao Dai](https://scholar.google.com/citations?user=fddAbqsAAAAJ&hl=en&oi=ao), [Xianjing Zhang](https://github.com/zoojing), Jintao Xu, Rui Ai, Weihao Gu, and [Xieyuanli Chen](https://github.com/Chen-Xieyuanli) -> *submitted to RAL/IROS 2022* +> *Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022* -![method-framework](assets/framework.png) + + +

+ + demo video + +


@@ -47,7 +56,7 @@ unzip ckpt_motionseg3d_pointrefine.zip ``` -Then you could use the follow command to inference and visualize the predictions. If you use toy dataset, please modify the `seq_id` corresponding to valid in `data_cfg.yaml`. +Then you could use the follow command to inference and visualize the predictions. If you use toy dataset, please modify the `seq_id` corresponding to `valid` in `model_path/data_cfg.yaml`. ```bash # To inference the predictions. python infer.py -d ./toydata -m ./log/motionseg3d_pointrefine -l ./pred/oursv1 -s valid @@ -63,7 +72,7 @@ python utils/visualize_mos.py -d ./toydata -p ./pred/oursv2 --offset 0 -s 38 2. Download KITTI Odometry Benchmark calibration data (1 MB) from [here](http://www.cvlibs.net/download.php?file=data_odometry_calib.zip). 3. Download SemanticKITTI label data (179 MB) (alternatively the data in Files corresponds to the same data) from [here](http://www.semantic-kitti.org/assets/data_odometry_labels.zip). 4. Download KITTI-Road Velodyne point clouds from [original website](http://www.cvlibs.net/datasets/kitti/raw_data.php?type=road), more details can be found in [config/kitti_road_mos.md](config/kitti_road_mos.md) -5. Download the KITTI-Road label data (5.4 MB) annotated by us from [here](https://drive.google.com/file/d/1BP-i6CoDCzuPNlcA7TUwsT24bXXgVIIY/view?usp=sharing). +5. Download the KITTI-Road-MOS label data annotated by us, the pose and calib files from [here](https://drive.google.com/file/d/1pdpcGReJHOJp01pbgXUbcGROWOBd_2kj/view?usp=sharing) (6.1 MB) . 6. Extract everything into the same folder, as follow:
[Expected directory structure of SemanticKITTI (click to expand)] @@ -90,7 +99,7 @@ DATAROOT
-7. Next run the data preparation script to generate the residual images. More parameters about the data preparation can be found in the yaml file [config/data_preparing.yaml](./config/data_preparing.yaml). +7. Next run the data preparation script (based on [LMNet](https://github.com/PRBonn/LiDAR-MOS)) to generate the residual images. More parameters about the data preparation can be found in the yaml file [config/data_preparing.yaml](./config/data_preparing.yaml). ```shell @@ -99,16 +108,17 @@ python utils/auto_gen_residual_images.py ### Inference on SemanticKITTI-MOS > The newly labeled KITTI-Road-MOS data is divided into train/valid set. -> The useage of data can be controlled by specifying data_config. +> The useage of data can be controlled by specifying `--data_config` in training. +> During inference, if you use toy dataset or do not download the KITTI-Road-MOS, please modify the `seq_id` corresponding to `valid` in `model_path/data_cfg.yaml`. ```bash # validation split -python infer.py -d DATAROOT -m ./log/model_path/logs/TIMESTAMP/ -l ./predictions/ -s valid # -dc config/labels/semantic-kitti-mos.yaml +python infer.py -d DATAROOT -m ./log/model_path/logs/TIMESTAMP/ -l ./predictions/ -s valid # test split -python infer.py -d DATAROOT -m ./log/model_path/logs/TIMESTAMP/ -l ./predictions/ -s test # -dc config/labels/semantic-kitti-mos.raw.yaml +python infer.py -d DATAROOT -m ./log/model_path/logs/TIMESTAMP/ -l ./predictions/ -s test ``` -The predictions/labels will be saved to `./predictions/`. `--data_config` determines whether to use new label data KITTI-Road. +The predictions/labels will be saved to `./predictions/`. ### Evaluation on SemanticKITTI-MOS validation split @@ -124,6 +134,7 @@ python utils/evaluate_mos.py -d DATAROOT -p ./predictions/ --datacfg config/labe ### Training on SemanticKITTI-MOS The training is seperated into two phases, and switching between phases is currently manually controlled. +`--data_config` determines whether to use new label data KITTI-Road-MOS, such as `-dc config/labels/semantic-kitti-mos.yaml` or `-dc config/labels/semantic-kitti-mos.raw.yaml` - Phase 1 (multi-gpu): Only the range image is used for input and supervision. The training log and checkpoint will be stored in `./log/ours_motionseg3d/logs/TIMESTAMP/`. @@ -145,10 +156,12 @@ python train_2stage.py -d DATAROOT -ac ./train_yaml/mos_pointrefine_stage.yml -l If you find this code useful for your research, please use the following BibTeX entry. ```bibtex -@article{sun2022mos3d, +@inproceedings{sun2022mos3d, title={Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation}, author={Sun, Jiadai and Dai, Yuchao and Zhang, Xianjing and Xu, Jintao and Ai, Rui and Gu, Weihao and Chen, Xieyuanli}, - year={2022} + booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, + year={2022}, + organization={IEEE} } ``` diff --git a/assets/visualization.jpg b/assets/visualization.jpg new file mode 100644 index 0000000000000000000000000000000000000000..9f4248462d0095497a692b325c9bbf7124fb0660 GIT binary patch literal 856295 zcmeFY2UL^IyDu7gFVc$;I!INdC?ElmCL*9h=qMdR5djGp2qMypfWRjRsB|e22)%?} 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2011_10_03_drive_0047 -> 41 ``` +We provide a simple download and conversion script [utils/download_kitti_road.sh](../utils/download_kitti_road.sh), please modify the `DATA_ROOT` path and manually move the result folder `sequences` to the target folder. +And you need to download the KITTI-Road-MOS label data annotated by us, the pose and calib files from [here](https://drive.google.com/file/d/1pdpcGReJHOJp01pbgXUbcGROWOBd_2kj/view?usp=sharing) (6.1 MB) . + We organize our proposed KITTI-Road-MOS using the same setup and data structure used in SemanticKITTI-MOS: ``` diff --git a/utils/download_kitti_road.sh b/utils/download_kitti_road.sh new file mode 100644 index 0000000..7b3c476 --- /dev/null +++ b/utils/download_kitti_road.sh @@ -0,0 +1,58 @@ +#!/bin/bash + +# raw_id -> seq_id +# 2011_09_26_drive_0015 -> 30 +# 2011_09_26_drive_0027 -> 31 +# 2011_09_26_drive_0028 -> 32 +# 2011_09_26_drive_0029 -> 33 +# 2011_09_26_drive_0032 -> 34 +# 2011_09_26_drive_0052 -> 35 +# 2011_09_26_drive_0070 -> 36 +# 2011_09_26_drive_0101 -> 37 +# 2011_09_29_drive_0004 -> 38 +# 2011_09_30_drive_0016 -> 39 +# 2011_10_03_drive_0042 -> 40 +# 2011_10_03_drive_0047 -> 41 + +road_raw_id_list=(2011_09_26_drive_0015 2011_09_26_drive_0027 2011_09_26_drive_0028 2011_09_26_drive_0029 + 2011_09_26_drive_0032 2011_09_26_drive_0052 2011_09_26_drive_0070 2011_09_26_drive_0101 + 2011_09_29_drive_0004 2011_09_30_drive_0016 2011_10_03_drive_0042 2011_10_03_drive_0047) +sub_id=(30 31 32 33 34 35 36 37 38 39 40 41) + +# Please modify it to the local path +DATA_ROOT=DEBUG_kitti_road + +mkdir $DATA_ROOT +cd $DATA_ROOT + +# wget -c https://s3.eu-central-1.amazonaws.com/avg-kitti/raw_data/2011_09_26_drive_0015/2011_09_26_drive_0015_sync.zip + +for i in $(seq 0 `expr ${#road_raw_id_list[@]} - 1`); do + raw_id=${road_raw_id_list[i]} + sub_id=${sub_id[i]} + + wget -c https://s3.eu-central-1.amazonaws.com/avg-kitti/raw_data/"$raw_id"/"$raw_id"_sync.zip + unzip "$raw_id"_sync.zip + mv "${raw_id:0:10}"/"$raw_id"_sync "$sub_id" + + cd $sub_id + mv velodyne_points/data velodyne + for vbin in velodyne/*; + do + if [ ${#vbin} != 23 ] + then + echo "[ != ] error, please check the folder" + break + fi + mv "$vbin" "${vbin:0:9}${vbin:13}" # 0000000077.bin --> 000077.bin + done + cd ../ + mkdir -p sequences/$sub_id + mv $sub_id/velodyne $sub_id/image_02 sequences/$sub_id + rm -rf $sub_id + +done + +# rm -rf 2011_09_26 2011_09_29 2011_09_30 2011_10_03 + +# Move the subfolder(30~41) under the `sequences` folder to the directory corresponding to SemanticKITTI-MOS \ No newline at end of file