beautyscribbles/selfsupervised_flow

Code for Paper "Self-Supervised LiDAR Scene Flow and Motion Segmentation"

Code for Paper: Self-Supervised LiDAR Scene Flow and Motion Segmentation

Stefan Baur^*, David Emmerichs^*, Frank Moosmann, Peter Pinggera, Björn Ommer and Andreas Geiger (^*: equal conribution)

[Paper] [Supplementary Material] [Project Page]

PRESENTATION VIDEOS

](https://youtu.be/z34H43BOEVw)

INSTALLATION

This repository has been developed and tested for

• Ubuntu 18.04
• CUDA 10.2
• CUDNN 7.6.5

First of all, clone the repository:

git clone --recursive

Download the datasets Kitti Raw, nuscenes, Kitti Scene Flow. Then, create folder with symlinks to your datasets (referred to as INPUT_DATADIR in the following) that you would like to use.

mkdir -p /path/to/my_datasets
cd /path/to/my_datasets
ln -s /path/to/nuscenes/dataset nuscenes
ln -s /path/to/kitti_raw kitti_raw
ln -s /path/to/kitti_scene_flow_data kitti_sf_data

Also, create a destination folder for the training logs (OUTPUT_DATADIR):

mkdir -p /path/to/output_destination

Change into the base directory of the repo and set INPUT_DATADIR (path to your datasets with the symlinks) and OUTPUT_DATADIR (path to logs) in scripts/set_env_variables.bash:

cd path/to/this/repo
vim scripts/set_env_variables.bash

Create the virtual environment, build tensorflow custom ops and set the necessary env variables: SRC_DIR, CFG_DIR, INPUT_DATADIR, OUTPUT_DATADIR.

# in root folder of this repository execute
python3 -m venv .venv --prompt usfl
source .venv/bin/activate
pip3 install -U setuptools pip
pip3 install -r requirements.txt


bash scripts/build_all.bash # builds TF custom ops
source scripts/set_env_variables.bash # sets environment variables

Also, you need to install the nuscenes-devkit.

Running experiments

The entry point for trainings is unsup_flow/cli.py. We use a unsup_flow/config/config.yml with incremental configurations to configure the experiments. The default configuration is loaded for every experiment.

# default experiment
python unsup_flow/cli.py --prod

Incremental config changes can be applied using the -c flag. For example, to run a training using the RAFT flow backbone on nuscenes without using the confidence weights for static aggregation:

python unsup_flow/cli.py --prod -c nuscenes sota_us sota_net no_stataggr_weight

This will, starting from the default config, recursively apply the changes to the config that are specified in the config file under sections nuscenes, sota_us, sota_net, no_stataggr_weight in that order.

Individual changes to the config can also be made using the -kv flag, for example to change the initial learning rate from 0.0001 (default) to 0.005:

python unsup_flow/cli.py --prod -c nuscenes -kv learning_rate initial 0.005

❗ NOTE: -kv takes a list of strings (e.g. learning_rate initial) and then a value (e.g. 0.005). The list of strings specify the config value location in the hierarchy of dictionaries!

Using your own dataset

Go to usfl_io/io_tools.py:1009 and implement a function that returns a TF Dataset, returning the following dict:

dict:
    name []
    pcl_t0 [N0, 3]
    ref_t0 [N0] (opt.)
    semantics_t0 [N0] (opt.)
    pcl_t1 [N1, 3]
    ref_t1 [N1] (opt.)
    semantics_t1 [N1] (opt.)
    odom_t0_t1 [4, 4] (opt.)
    flow_gt_t0_t1 dict (opt.):
        flow [N0, 3]
        annotation_mask [N0]
        nn_interpolated_mask [N0]
        exact_gt_mask [N0]
        ego_flow_mask [N0] (opt.)
    flow_gt_t1_t0 dict (opt.):
        flow [N1, 3]
        annotation_mask [N1]
        nn_interpolated_mask [N1]
        exact_gt_mask [N1]
        ego_flow_mask [N1] (opt.)

You can use one of the existing datasets for inspiration. Most of the above data is optional (opt.) and will be used to generate informative metrics.

Coordinate system: Positive z-Axis is opposite to the direction of gravity, x is forward, y to the left. (Right handed coordinate system.) The origin should be around 1.7m above ground and in the center of the recording sensor.

##FOR CONTRIBUTIONS: For this, use the dataset creation scripts in the folder here: unsup_flow/datasets/ where you can find each create.py script for the respective datasets. Run each script once to convert the datasets into our format.

CITE

@InProceedings{Baur_2021_ICCV,
   author    = {Baur, Stefan Andreas and Emmerichs, David Josef and Moosmann, Frank and Pinggera, Peter and Ommer, Bj\"orn and Geiger, Andreas},
   title     = {SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation},
   booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
   month     = {October},
   year      = {2021},
   pages     = {13126-13136}
}

LEGAL CONTRIBUTIONS

So the code has been tested solely for our use cases, which might differ from yours. Notice: Before you use the program in productive use, please take all necessary precautions, e.g. testing and verifying the program with regard to your specific use.

Code of Conduct Contribution Guidelines