PS3-SemanticKITTI

Paired sparse-dense synthetic scenes for LiDAR semantic scene completion.

Every dense semantic scene in this corpus is paired with a matched sparse LiDAR observation of the same scene, so a model can be trained on the sparse-to-dense mapping without either half having been recorded by a real sensor.

What is in the deposit

Archive	Scenes	Compressed
synthetic_pool_31K.tar.gz	32,039	~2.2 GiB
synthetic_pool_57K.tar.gz	57,650	~3.9 GiB

Both archives are also mirrored on Hugging Face, byte for byte identical to the copies deposited here and downloadable without an IEEE DataPort subscription:

https://huggingface.co/datasets/Stone-Chern/PS3-SemanticKITTI

The mirror becomes public alongside the reference code linked at the end of this file. Cite the DOI whichever copy you download: 10.21227/nqgf-9k39 is this dataset's citable identifier of record, and the mirror carries the same LICENSE.

Take the 31K pool unless you specifically want the larger variant. It is the pool the accompanying work trains on for its reported results.

synthetic_pool_31K is a strict subset of synthetic_pool_57K, verified over all 96,117 of its files, each resolving to the same content. Downloading both duplicates 32,039 scenes.

The accompanying work reports the larger pool as a negative result: scaling beyond 32,039 scenes did not improve accuracy. The 57K archive is released so that finding can be checked, not because more data is better here.

Layout

Each archive extracts to a flat directory holding three NumPy arrays per scene.

<id>_voxels.npy     (256, 256, 32)  uint8   sparse binary occupancy
<id>_gt_scene.npy   (256, 256, 32)  uint8   dense semantic labels, 0-19
<id>_bev.npy        (256, 256)      uint8   bird's-eye-view map, 0-19

Each archive also contains LICENSE and generation_metadata.json.

_voxels.npy is strictly binary, with values {0, 1}, and marks which voxels the simulated sensor returned. _gt_scene.npy is the complete scene from which that sweep was traced.

import numpy as np

sweep = np.load("000000_voxels.npy")    # (256, 256, 32) uint8, {0, 1}
dense = np.load("000000_gt_scene.npy")  # (256, 256, 32) uint8, 0..19
bev = np.load("000000_bev.npy")         # (256, 256)     uint8, 0..19

Classes

Label 0 is free or unlabelled space. Labels 1-19 follow the SemanticKITTI learning map.

Label	Class	Label	Class
0	free / unlabelled	10	parking
1	car	11	sidewalk
2	bicycle	12	other-ground
3	motorcycle	13	building
4	truck	14	fence
5	other-vehicle	15	vegetation
6	person	16	trunk
7	bicyclist	17	terrain
8	motorcyclist	18	pole
9	road	19	traffic-sign

This is the standard 20-way learning map, so the labels are directly comparable with SemanticKITTI annotations and with the output of models trained on them.

How the scenes were made

1. Generation. Three cascaded coarse-to-fine multinomial diffusion models, at 32×32×4, then 64×64×8, then 256×256×32, trained on the SemanticKITTI training split.
2. Screening. Each candidate's class histogram is compared against a corpus prior estimated on held-out real scenes, and a scene is kept only where the Jensen-Shannon divergence falls below a threshold. This is what stops the generator drifting toward implausible class mixtures.
3. Rare-class insertion. Surviving scenes receive objects from a rare-class object bank, placed on ground-level voxels.
4. Sensor simulation. A hardware-aware HDL-64E ray-tracer converts each dense scene into the sparse sweep that sensor would have returned. Pasting happens before ray-tracing, so the sparse half of each pair genuinely contains the inserted rare classes.

The pipeline exists for the long tail. It produces supervision for the scarcest classes at source, rather than reweighting a distribution that never contained enough of them.

generation_metadata.json inside each archive records that pool's aggregate provenance. For the 57K pool, per-batch generation records were not retained. The one surviving batch record covers 2,055 scenes and ships alongside as generation_metadata_batch_seed2024.json; it does not describe the pool, and the metadata file says so.

Licence and attribution

Released under CC BY-NC-SA 4.0. This is inherited rather than chosen: the corpus is derived from SemanticKITTI, which carries the same terms, so the non-commercial and share-alike conditions follow through to this data and to anything derived from it.

Cite this dataset as:

Shi Chen, Weifeng Ge, "PS3-SemanticKITTI: Paired Sparse-Dense Synthetic Scenes for LiDAR Semantic Scene Completion", IEEE Dataport, August 23, 2026, doi:10.21227/nqgf-9k39

@data{nqgf-9k39-26,
  doi       = {10.21227/nqgf-9k39},
  url       = {https://dx.doi.org/10.21227/nqgf-9k39},
  author    = {Shi Chen and Weifeng Ge},
  publisher = {IEEE Dataport},
  title     = {PS3-SemanticKITTI: Paired Sparse-Dense Synthetic Scenes for
               LiDAR Semantic Scene Completion},
  year      = {2026}
}

@data is IEEE Dataport's own entry type; substitute @misc if your bibliography style does not know it.

Any use must also cite both of the papers that SemanticKITTI requires:

1. J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss and J. Gall. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. ICCV 2019.
2. A. Geiger, P. Lenz and R. Urtasun. Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. CVPR 2012, pp. 3354-3361.

Reference code

The loader, the training configurations these pools were built for, and the evaluation protocol are at github.com/BillyChern/GSSC-S2D2.