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SemanticKITTI

Two options

  1. Direct downloading

    • The semantic scene completion dataset v1.1 (SemanticKITTI voxel data, 700 MB) from SemanticKITTI website.
    • The RGB images (Download odometry data set (color, 65 GB)) from KITTI Odometry website.
    • The calibration and pose files from voxformer/preprocess/data_odometry_calib/sequences.
    • The preprocessed ground truth (~700MB) from labels.
    • The voxelized psuedo point cloud and query proposals (~400MB) based on MobileStereoNet from sequences_msnet3d_sweep10.

  2. Downloading the voxel and image data first, then following the commands in voxformer/preprocess to create labels and sequences_msnet3d_sweep10. You need to choose this option if you would like to use different data or depth models.

Folder structure

The data is organized in the following format:

/kitti/dataset/
          └── sequences/
          │       ├── 00/
          │       │   ├── poses.txt
          │       │   ├── calib.txt
          │       │   ├── image_2/
          │       │   ├── image_3/
          │       |   ├── voxels/
          │       |         ├ 000000.bin
          │       |         ├ 000000.label
          │       |         ├ 000000.occluded
          │       |         ├ 000000.invalid
          │       |         ├ 000005.bin
          │       |         ├ 000005.label
          │       |         ├ 000005.occluded
          │       |         ├ 000005.invalid
          │       ├── 01/
          │       ├── 02/
          │       .
          │       └── 21/
          └── labels/
          │       ├── 00/
          │       │   ├── 000000_1_1.npy
          │       │   ├── 000000_1_2.npy
          │       │   ├── 000005_1_1.npy
          │       │   ├── 000005_1_2.npy
          │       ├── 01/
          │       .
          │       └── 10/
          └── sequences_msnet3d_sweep10/
                  ├── 00/
                  │   ├── voxels/
                  │   │     ├ 000000.pseudo
                  │   │     ├ 000005.pseudo
                  │   ├── queries/
                  │   │     ├ 000000.query
                  │   │     ├ 000005.query
                  ├── 01/
                  ├── 02/
                  .
                  └── 21/