This repository contains helper scripts to open, visualize, process, and evaluate results for point clouds and labels from the SemanticKITTI dataset.
- Link to original KITTI Odometry Benchmark Dataset
- Link to SemanticKITTI dataset.
- Link to SemanticKITTI benchmark competition.
The data is organized in the following format:
/kitti/dataset/
└── sequences/
├── 00/
│ ├── poses.txt
│ ├── image_2/
│ ├── image_3/
│ ├── labels/
│ │ ├ 000000.label
│ │ └ 000001.label
| ├── voxels/
| | ├ 000000.bin
| | ├ 000000.label
| | ├ 000000.occluded
| | ├ 000000.invalid
| | ├ 000001.bin
| | ├ 000001.label
| | ├ 000001.occluded
| | ├ 000001.invalid
│ └── velodyne/
│ ├ 000000.bin
│ └ 000001.bin
├── 01/
├── 02/
.
.
.
└── 21/
- From KITTI Odometry:
image_2
andimage_3
correspond to the rgb images for each sequence.velodyne
contains the pointclouds for each scan in each sequence. Each.bin
scan is a list of float32 points in [x,y,z,remission] format. See laserscan.py to see how the points are read.
- From SemanticKITTI:
labels
contains the labels for each scan in each sequence. Each.label
file contains a uint32 label for each point in the corresponding.bin
scan. See laserscan.py to see how the labels are read.poses.txt
contain the manually looped-closed poses for each capture (in the camera frame) that were used in the annotation tools to aggregate all the point clouds.voxels
contains all information needed for the task of semantic scene completion. Each.bin
file contains for each voxel if that voxel is occupied by laser measurements in a packed binary format. This is the input to the semantic scene completion task and it corresponds to the voxelization of a single LiDAR scan. Each.label
file contains for each voxel of the completed scene a label in binary format. The label is a 16-bit unsigned integer (aka uint16_t) for each voxel..invalid
and.occluded
contain information about the occlusion of voxel. Invalid voxels are voxels that are occluded from each view position and occluded voxels are occluded in the first view point. See also SSCDataset.py for more information on loading the data.
The main configuration file for the data is in config/semantic-kitti.yaml
. In this file you will find:
labels
: dictionary which maps numeric labels in.label
file to a string class. Example:10: "car"
color_map
: dictionary which maps numeric labels in.label
file to a bgr color for visualization. Example10: [245, 150, 100] # car, blue-ish
content
: dictionary with content of each class in labels, as a ratio to the number of total points in the dataset. This can be obtained by running the ./content.py script, and is used to calculate the weights for the cross entropy in all baseline methods (in order handle class imbalance).learning_map
: dictionary which maps each class label to its cross entropy equivalent, for learning. This is done to mask undesired classes, map different classes together, and because the cross entropy expects a value in [0, numclasses - 1]. We also provide ./remap_semantic_labels.py, a script that uses this dictionary to put the label files in the cross entropy format, so that you can use the labels directly in your training pipeline. Examples:0 : 0 # "unlabeled" 1 : 0 # "outlier" to "unlabeled" -> gets ignored in training, with unlabeled 10: 1 # "car" 252: 1 # "moving-car" to "car" -> gets merged with static car class
learning_map_inv
: dictionary with inverse of the previous mapping, allows to map back the classes only to the interest ones (for saving point cloud predictions in original label format). We also provide ./remap_semantic_labels.py, a script that uses this dictionary to put the label files in the original format, when instantiated with the--inverse
flag.learning_ignore
: dictionary that contains for each cross entropy class if it will be ignored during training and evaluation or not. For example, classunlabeled
gets ignored in both training and evaluation.split
: contains 3 lists, with the sequence numbers for training, validation, and evaluation.
System dependencies
$ sudo apt install python3-dev python3-pip python3-pyqt5.qtopengl # for visualization
Python dependencies
$ sudo pip3 install -r requirements.txt
ALL OF THE SCRIPTS CAN BE INVOKED WITH THE --help (-h) FLAG, FOR EXTRA INFORMATION AND OPTIONS.
To visualize the data, use the visualize.py
script. It will open an interactive
opengl visualization of the pointclouds along with a spherical projection of
each scan into a 64 x 1024 image.
$ ./visualize.py --sequence 00 --dataset /path/to/kitti/dataset/
where:
sequence
is the sequence to be accessed.dataset
is the path to the kitti dataset where thesequences
directory is.
Navigation:
n
is next scan,b
is previous scan,esc
orq
exits.
In order to visualize your predictions instead, the --predictions
option replaces
visualization of the labels with the visualization of your predictions:
$ ./visualize.py --sequence 00 --dataset /path/to/kitti/dataset/ --predictions /path/to/your/predictions
To directly compare two sets of data, use the compare.py
script. It will open an interactive
opengl visualization of the pointcloud labels.
$ ./compare.py --sequence 00 --dataset_a /path/to/dataset_a/ --dataset_b /path/to/kitti/dataset_b/
where:
sequence
is the sequence to be accessed.dataset_a
is the path to a dataset in KITTI format where thesequences
directory is.dataset_b
is the path to another dataset in KITTI format where thesequences
directory is.
Navigation:
n
is next scan,b
is previous scan,esc
orq
exits.
To visualize the data, use the visualize_voxels.py
script. It will open an interactive
opengl visualization of the voxel grids and options to visualize the provided voxelizations
of the LiDAR data.
$ ./visualize_voxels.py --sequence 00 --dataset /path/to/kitti/dataset/
where:
sequence
is the sequence to be accessed.dataset
is the path to the kitti dataset where thesequences
directory is.
Navigation:
n
is next scan,b
is previous scan,esc
orq
exits.
Note: Holding the forward/backward buttons triggers the playback mode.
LiDAR-based Moving Object Segmentation (LiDAR-MOS)
To visualize the data, use the visualize_mos.py
script. It will open an interactive
opengl visualization of the voxel grids and options to visualize the provided voxelizations
of the LiDAR data.
$ ./visualize_mos.py --sequence 00 --dataset /path/to/kitti/dataset/
where:
sequence
is the sequence to be accessed.dataset
is the path to the kitti dataset where thesequences
directory is.
Navigation:
n
is next scan,b
is previous scan,esc
orq
exits.
Note: Holding the forward/backward buttons triggers the playback mode.
To evaluate the predictions of a method, use the evaluate_semantics.py to evaluate
semantic segmentation, evaluate_completion.py to evaluate the semantic scene completion and evaluate_panoptic.py to evaluate panoptic segmentation.
Important: The labels and the predictions need to be in the original
label format, which means that if a method learns the cross-entropy mapped
classes, they need to be passed through the learning_map_inv
dictionary
to be sent to the original dataset format. This is to prevent changes in the
dataset interest classes from affecting intermediate outputs of approaches,
since the original labels will stay the same.
For semantic segmentation, we provide the remap_semantic_labels.py
script to make this
shift before the training, and once again before the evaluation, selecting which are the interest
classes in the configuration file.
The data needs to be either:
-
In a separate directory with this format:
/method_predictions/ └── sequences ├── 00 │ └── predictions │ ├ 000000.label │ └ 000001.label ├── 01 ├── 02 . . . └── 21
And run:
$ ./evaluate_semantics.py --dataset /path/to/kitti/dataset/ --predictions /path/to/method_predictions --split train/valid/test # depending of desired split to evaluate
or
$ ./evaluate_completion.py --dataset /path/to/kitti/dataset/ --predictions /path/to/method_predictions --split train/valid/test # depending of desired split to evaluate
or
$ ./evaluate_panoptic.py --dataset /path/to/kitti/dataset/ --predictions /path/to/method_predictions --split train/valid/test # depending of desired split to evaluate
or for moving object segmentation
$ ./evaluate_mos.py --dataset /path/to/kitti/dataset/ --predictions /path/to/method_predictions --split train/valid/test # depending of desired split to evaluate
-
In the same directory as the dataset
/kitti/dataset/ ├── poses └── sequences ├── 00 │ ├── image_2 │ ├── image_3 │ ├── labels │ │ ├ 000000.label │ │ └ 000001.label │ ├── predictions │ │ ├ 000000.label │ │ └ 000001.label │ └── velodyne │ ├ 000000.bin │ └ 000001.bin ├── 01 ├── 02 . . . └── 21
And run (which sets the predictions directory as the same directory as the dataset):
$ ./evaluate_semantics.py --dataset /path/to/kitti/dataset/ --split train/valid/test # depending of desired split to evaluate
If instead, the IoU vs distance is wanted, the evaluation is performed in the
same way, but with the evaluate_semantics_by_distance.py script. This will
analyze the IoU for a set of 5 distance ranges: {(0m:10m), [10m:20m), [20m:30m), [30m:40m), (40m:50m)}
.
To ensure that your zip file is valid, we provide a small validation script validate_submission.py that checks for the correct folder structure and consistent number of labels for each scan.
The submission folder expects to get an zip file containing the following folder structure (as the separate case above)
├ description.txt (optional)
sequences
├── 11
│ └── predictions
│ ├ 000000.label
│ ├ 000001.label
│ ├ ...
├── 12
│ └── predictions
│ ├ 000000.label
│ ├ 000001.label
│ ├ ...
├── 13
.
.
.
└── 21
In summary, you only have to provide the label files containing your predictions for every point of the scan and this is also checked by our validation script.
Run:
$ ./validate_submission.py --task {segmentation|completion|panoptic} /path/to/submission.zip /path/to/kitti/dataset
to check your submission.zip
.
Note: We don't check if the labels are valid, since invalid labels are simply ignored by the evaluation script.
If you want to have more information on the leaderboard in the new updated Codalab competitions under the "Detailed Results", you have to provide an additional description.txt
file to the submission archive containing information (here just an example):
name: Auto-MOS
pdf url: https://arxiv.org/pdf/2201.04501.pdf
code url: https://github.com/PRBonn/auto-mos
where name
corresponds to the name of the method, pdf url
is a link to the paper pdf url (or empty), and code url
is a url that directs to the code (or empty). If the information is not available, we will use Anonymous
for the name, and n/a
for the urls.
- content.py allows to evaluate the class content of the training set, in order to weigh the loss for training, handling imbalanced data.
- count.py returns the scan count for each sequence in the data.
-
generate_sequential.py generates a sequence of scans using the manually looped closed poses used in our labeling tool, and stores them as individual point clouds. If, for example, we want to generate a dataset containing, for each point cloud, the aggregation of itself with the previous 4 scans, then:
$ ./generate_sequential.py --dataset /path/to/kitti/dataset/ --sequence_length 5 --output /path/to/put/new/dataset
-
remap_semantic_labels.py allows to remap the labels to and from the cross-entropy format, so that the labels can be used for training, and the predictions can be used for evaluation. This file uses the
learning_map
andlearning_map_inv
dictionaries from the config file to map the labels and predictions.
If not installing the requirements is preferred, then a docker container is provided to run the scripts.
To build and run the container in an interactive session, which allows to run X11 apps (and GL), and copies this repo to the working directory, use
$ ./docker.sh /path/to/dataset
Where /path/to/dataset
is the location of your semantic kitti dataset, and
will be available inside the image in ~/data
or /home/developer/data
inside the container for further usage with the api. This is done by creating
a shared volume, so it can be any directory containing data that is to be used
by the API scripts.
If you use this dataset and/or this API in your work, please cite its paper
@inproceedings{behley2019iccv,
author = {J. Behley and M. Garbade and A. Milioto and J. Quenzel and S. Behnke and C. Stachniss and J. Gall},
title = {{SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences}},
booktitle = {Proc. of the IEEE/CVF International Conf.~on Computer Vision (ICCV)},
year = {2019}
}
And the paper for the original KITTI dataset:
@inproceedings{geiger2012cvpr,
author = {A. Geiger and P. Lenz and R. Urtasun},
title = {{Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite}},
booktitle = {Proc.~of the IEEE Conf.~on Computer Vision and Pattern Recognition (CVPR)},
pages = {3354--3361},
year = {2012}}