kitti-dataset

Crates.iokitti-dataset
lib.rskitti-dataset
version0.3.0
sourcesrc
created_at2023-09-21 02:37:21.11627
updated_at2024-04-06 16:47:44.571976
descriptionDataset loader, data parsers and writers for KITTI dataset.
homepagehttps://github.com/jerry73204/kitti-dataset-for-rust
repositoryhttps://github.com/jerry73204/kitti-dataset-for-rust.git
max_upload_size
id979041
size1,215,149
(jerry73204)

documentation

https://docs.rs/kitti-dataset/

README

Ser/Deserialization for KITTI dataset written in Rust

This project provides essential data types and their ser/deserialization implementations.

Dataset API

The dataset loader allows you to iterate through all kinds of data samples. Currently, ObjectDataset and TrackingDataset are supported.

The dataset layout for Object Detection Evaluation 2012 dataset is presented below for example. You can download appropriate zip files on the official site and extract them together to get the layout.

object/training
├── calib
├── image_2
├── image_3
├── label_2
└── velodyne

The usage of dataset API is demonstrated in the code.

use kitti_dataset::dataset::{object::SampleData, ObjectDataset};

let dataset = ObjectDataset::open("/path/to/kitti_dir/object/training")?;

// To get a specific frame
let frame = dataset.frame(0).unwrap();

// Iterate through all frames
for frame in dataset.frame_iter() {
    // Obtain a specific sample
    let sample = frame.key("image_2").unwrap();
    let SampleData::Image(image) = sample.data()? else {
        unreachable!();
    };

    // Iterate through all samples
    for sample in frame.sample_iter() {
        let data = sample.data()?;
    }
}

Supported Types

  • Annotation - 3D Object Detection Evaluation 2017 labels

    car 0.00 0 -1.58 587.01 173.33 614.12 200.12 1.65 1.67 3.64 -0.65 1.71 46.70 -1.59
    cyclist 0.00 0 -2.46 665.45 160.00 717.93 217.99 1.72 0.47 1.65 2.45 1.35 22.10 -2.35
    pedestrian 0.00 2 0.21 423.17 173.67 433.17 224.03 1.60 0.38 0.30 -5.87 1.63 23.11 -0.03
    
  • CameraCalibration - 3D Object Detection Evaluation 2017 camera calibration matrices

    P0: 7.070493000000e+02 0.000000000000e+00 6.040814000000e+02 0.000000000000e+00 0.000000000000e+00 7.070493000000e+02 1.805066000000e+02 0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 0.000000000000e+00
    P1: 7.070493000000e+02 0.000000000000e+00 6.040814000000e+02 -3.797842000000e+02 0.000000000000e+00 7.070493000000e+02 1.805066000000e+02 0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 0.000000000000e+00
    P2: 7.070493000000e+02 0.000000000000e+00 6.040814000000e+02 4.575831000000e+01 0.000000000000e+00 7.070493000000e+02 1.805066000000e+02 -3.454157000000e-01 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 4.981016000000e-03
    P3: 7.070493000000e+02 0.000000000000e+00 6.040814000000e+02 -3.341081000000e+02 0.000000000000e+00 7.070493000000e+02 1.805066000000e+02 2.330660000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 3.201153000000e-03
    R0_rect: 9.999128000000e-01 1.009263000000e-02 -8.511932000000e-03 -1.012729000000e-02 9.999406000000e-01 -4.037671000000e-03 8.470675000000e-03 4.123522000000e-03 9.999556000000e-01
    Tr_velo_to_cam: 6.927964000000e-03 -9.999722000000e-01 -2.757829000000e-03 -2.457729000000e-02 -1.162982000000e-03 2.749836000000e-03 -9.999955000000e-01 -6.127237000000e-02 9.999753000000e-01 6.931141000000e-03 -1.143899000000e-03 -3.321029000000e-01
    Tr_imu_to_velo: 9.999976000000e-01 7.553071000000e-04 -2.035826000000e-03 -8.086759000000e-01 -7.854027000000e-04 9.998898000000e-01 -1.482298000000e-02 3.195559000000e-01 2.024406000000e-03 1.482454000000e-02 9.998881000000e-01 -7.997231000000e-01
    
  • OdometryCalibration - Visual Odometry / SLAM Evaluation 2012 calibration files

    P0: 7.188560000000e+02 0.000000000000e+00 6.071928000000e+02 0.000000000000e+00 0.000000000000e+00 7.188560000000e+02 1.852157000000e+02 0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 0.000000000000e+00
    P1: 7.188560000000e+02 0.000000000000e+00 6.071928000000e+02 -3.861448000000e+02 0.000000000000e+00 7.188560000000e+02 1.852157000000e+02 0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 0.000000000000e+00
    P2: 7.188560000000e+02 0.000000000000e+00 6.071928000000e+02 4.538225000000e+01 0.000000000000e+00 7.188560000000e+02 1.852157000000e+02 -1.130887000000e-01 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 3.779761000000e-03
    P3: 7.188560000000e+02 0.000000000000e+00 6.071928000000e+02 -3.372877000000e+02 0.000000000000e+00 7.188560000000e+02 1.852157000000e+02 2.369057000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 4.915215000000e-03
    Tr: 4.276802385584e-04 -9.999672484946e-01 -8.084491683471e-03 -1.198459927713e-02 -7.210626507497e-03 8.081198471645e-03 -9.999413164504e-01 -5.403984729748e-02 9.999738645903e-01 4.859485810390e-04 -7.206933692422e-03 -2.921968648686e-01
    
  • Point - 3D Object Detection Evaluation 2017 Velodyne point clouds

License

This software is distributed under MIT license. Read the LICENSE.txt file to see the full license.

Commit count: 36

cargo fmt