SeeingThroughFog

Paper Link

We introduce a object detection benchmark in challenging adverse weather conditions covering 12000 samples in real world driving scenes and 1500 samples in controlled weather conditions within a fog chamber. The dataset covers different weather conditions as Fog, Snow and Rain and was acquired by over 10,000 km of driving in northern Europe. The driven route with cities along the route are shown below. In total 100k Objekts where labeled with accurate 2D and 3D bounding boxes. Main contributions of this dataset are:

• We provide a proving ground for a broad range of algorithms covering signal enhancement, domain adaptation, object detection or multi-modal sensor fusion focusing on the learning of robust redundancies in between sensor especially if they fail asymmetrically in different weather conditions.
• The dataset was created with the initial intention to showcase methods learning robust redundancies in between sensor enabling raw data sensor fusion in case of asymmetric sensor failure induced through adverse weather effects. In our case we departed from proposal level fusion and applied a adaptive fusion driven by measurement entropy enabling the detection also in case of unknown adverse weather effects. This method outperforms other reference fusion methods, which even drop in below single image methods.
• Furthermore, we compared the method to variety of related methods as domain adaptation, signal enhancement and adverse weather simulation. Please see paper for more information.

Videos

Real world recording in dense fog.

Real world recording in heavy snowfall.

Real world recording in snow dust.

Getting Started

Clone the benchmark code.

git clone https://github.com/princeton-computational-imaging/SeeingThroughFog
cd SeeingThroughFog

For running the evaluation and visualization code you need Python with the following packages:

• tensorflow
• numpy
• cv2
• matplotlib
• qt
• pyqtgraph
• pyquaternion

We provide a conda environment to run our code.

conda env create -f environment.yaml

Activate the conda environment.

conda activate LabelTool

Download the SeeingThroughFog dataset from the DENSE dataset webpage. Please download the zip files per sensor and copy all sensorfolders into one common dataset folder.

After unzipping the files, your directory should look like this:

.
|-- SeeingThroughFogData
    |-- cam_stereo_left
        |-- *.tiff
        |-- ...
    |-- cam_stereo_left_lut
        |-- *.png
        |-- ...
    |-- lidar_hdl64_last
        |-- *.bin
        |-- ...
    |-- lidar_hdl64_strongest
        |-- *.bin
        |-- ...
    |-- ...

Each folder has following meaning:

Folder	Extrinsic Coordinate System	Necessary for DataViewer	Description
cam_stereo_left	cam_stereo_left_optical	-	12-bit RGB images captured from the left stereo camera.
cam_stereo_left_lut	cam_stereo_left_optical	x	8-bit RGB left stereo camera images used for annotating the dataset, with custom tone mapping presented in tools/Raw2LUTImages/main.py.
cam_stereo_right	cam_stereo_right_optical	-	12-bit RGB images captured from the right stereo camera.
cam_stereo_right_lut	cam_stereo_right_optical	x	8-bit RGB right stereo camera images with custom tone mapping.
cam_stereo_sgm	-	-	Stereo disparity calculated using Stereo Global Matching (SGM).
cam_stereo_left_raw_history_<d>	cam_stereo_left_optical	-	Temporal History data for <d> in [-5,3] which represents the relativ offset to the annotated samples in cam_stereo_left.
fir_axis	fir_axis_roof_optical	-	8-bit FIR reference camera.
gated<d>_raw	bwv_cam_optical	-	10-bit NIR gated camera image for slice index <d>.
gated<d>_rect	bwv_cam_optical	x	10-bit rectified NIR gated camera image for slice index <d>.
gated<d>_8bit	bwv_cam_optical	-	8-bit rectified bitshifted NIR gated camera image for slice index <d> used for dataset annotation.
gated_full_rect	bwv_cam_optical	x	10-bit NIR gated camera image with joint slices from a single capture.
gated_full_rect8	bwv_cam_optical	-	8-bit rectified bitshifted NIR gated camera image with joint slices from a single capture.
gated_full_acc_rect	bwv_cam_optical	-	10-bit NIR gated camera image with overlayed gated slices from gated<d>_raw.
gated_full_acc_rect8	bwv_cam_optical	x	8-bit rectified bitshifted NIR gated camera image with overlayed gated slices from gated<d>_8bit.
radar_targets	radar	-	Radar target pointcloud.
lidar_hdl64_last	lidar_hdl64_s3_roof	x	Lidar pointcloud acquired from the Velodyne HDL64-S3D containing the last echo.
lidar_hdl64_last_gated	bwv_cam_optical	x	Lidar pointcloud acquired from the Velodyne HDL64-S3D containing the last echo projected into gated camera.
lidar_hdl64_last_stereo_left	cam_stereo_left_optical	x	Lidar pointcloud acquired from the Velodyne HDL64-S3D containing the last echo projected into left stereo image.
lidar_hdl64_strongest	lidar_hdl64_s3_roof	x	Lidar pointcloud acquired from the Velodyne HDL64-S3D containing the strongest echo.
lidar_hdl64_strongest_gated	bwv_cam_optical	x	Lidar pointcloud acquired from the Velodyne HDL64-S3D containing the strongest echo projected into gated camera.
lidar_hdl64_strongest_stereo_left	cam_stereo_left_optical	x	Lidar pointcloud acquired from the Velodyne HDL64-S3D containing the strongest echo projected into left stereo image.
lidar_vlp32_last	lidar_vlp32_roof	-	Lidar pointcloud acquired from the Velodyne VLP32 containing the last echo.
lidar_vlp32_strongest	lidar_vlp32_roof	-	Lidar pointcloud acquired from the Velodyne VLP32 containing the strongest echo.
velodyne_planes	-	-	Lidar groundplanes estimated from the lidar_hdl64_strongest measurements.
road_friction	-	x	Road friction measurements.
weather_station	-	x	Ambient temperature, humidity, dew point, ...
labeltool_labels	-	x	Meta labels on ambient illumination, drivable patch, scene setting and weather.
filtered_relevant_can_data	-	x	Vehicle data on wiper state, speed, ...
gt_labels	-	x	3D annotation labels following the Kitti annotation layout. cam_left_labels_TMP contains the labels inside the stereo camera left coordinate system and gated_labels_TMP in the gated camera coordinate system.

The extrinsic coordinate transormations in the third column are given in tools/DatasetViewer/calibs/calib_tf_tree_full.json. It follows the general tansformation logic from ROS.

For an initial reasoning about the dataset the DataViwer in tools/DatasetViewer/DataViewer_V2.py can be used. The needed data is marked in the table above.

Sensor Setup

To total we have equipped a test vehicle with sensors covering the visible, mm-wave, NIR and FIR band, see Figure below. We measure intensity, depth and weather condition. For more information please refer to our dataset paper.

Labeling

The final dataset provides about 12000 samples from an extensive data collection campaing with in total 1.4 mio samples. The subsampling process is described in depth within the supplemental material. Which can be found here. We provide 2d and 3d annotations for 4 main classes PassengerCars, Pedestrians, RidableVehicles, LargeVehicles. If PassengerCars, RidableVehicles and LargeVehicles can not be differentiated the Objects are Labeled within the fallback class Vehicle. In case Vehicle and Pedestrians can not be differentiated Objects are labeled as Obstacles. Below you can find the illustrated LabelDefinition:

The Annotation format follows the well known Kitti Format with added coloums for 3d Box Rotation angles. You can find the loading functionalities in

./tools/DatasetViewer/lib/read.py

Recomended data splits can be found in

./splits

Tools

Tools can be found in

"./SeeingThroughFog/tools/".

The tools help to viszualize the dataset (DatasetViewer), create a TFRecords dataset (CreateTFRecords), create simple fog simulations for lidar and rgb data (DatasetFoggification) and to quickly reason about the dataset statistics (DatasetStatisticsTool).

Reference

If you find our work on object detection in adverse weather useful in your research, please consider citing our paper:

@InProceedings{Bijelic_2020_STF,
    author={Bijelic, Mario and Gruber, Tobias and Mannan, Fahim and Kraus, Florian and Ritter, Werner and Dietmayer, Klaus and Heide, Felix},
    title={Seeing Through Fog Without Seeing Fog:
    Deep Multimodal Sensor Fusion in Unseen Adverse Weather},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2020}
}

Acknowledgements

These works have received funding from the European Union under the H2020 ECSEL Programme as part of the DENSE project, contract number 692449.

Feedback/Questions/Error reporting

Feedback? Questions? Any problems/errors? Do not hesitate to contact us!