Stone-Chern/GSSC-S2D2-datasets

GSSC-S2D2 — baseline predictions and rare-class object bank

Supporting artefacts for the GSSC / S²D² work on LiDAR semantic scene completion. This repository holds the frozen baseline predictions that S²D² refines, and the rare-class object bank used by the PS³ augmentation pipeline.

Looking for the dataset itself? The paired sparse–dense synthetic scenes are a different release: Stone-Chern/PS3-SemanticKITTI, mirroring IEEE DataPort 10.21227/nqgf-9k39. That is the one to start with.

Why archives rather than per-frame files

Uncompressed this corpus is 609,349 files and roughly 600 GB, with several directories at the Hugging Face limit of 10,000 files per directory. The per-frame data compresses about 110:1, so it ships as ten zstd archives totalling 5.4 GB. Every one of the 609,349 original files is present; the member count of each archive is verified against the source tree.

Contents

Archive	Files	Size	What it is
scpnet_predictions_sequences.tar.zst	81,308	737 MB	SCPNet predictions, real KITTI sequences 00–21
scpnet_predictions_synthetic.tar.zst	174,063	1.14 GB	SCPNet predictions, full synthetic pool
scpnet_predictions_synthetic_31k.tar.zst	96,117	653 MB	SCPNet predictions, 31K pool
scpnet_predictions_synthetic_30000.tar.zst	59,998	603 MB	SCPNet predictions, 30K subset
scpnet_predictions_synthetic_10000.tar.zst	19,998	200 MB	SCPNet predictions, 10K subset
js3cnet_predictions_sequences.tar.zst	27,105	1.15 GB	JS3C-Net predictions, real sequences 00–21
js3cnet_predictions_synthetic_filtered.tar.zst	38,322	162 MB	JS3C-Net predictions, filtered synthetic pool
js3cnet_predictions_synthetic_31k.tar.zst	31,442	130 MB	JS3C-Net predictions, 31K pool
lmscnet_predictions.tar.zst	23,203	876 MB	LMSCNet predictions, real sequences
object_bank.tar.zst	57,793	48 MB	Rare-class object bank (8 classes) used by PS³

Each archive carries its own README.md and LICENSE from the source tree.

Extracting

# one archive
tar -I zstd -xf scpnet_predictions_sequences.tar.zst

# or fetch just what you need
huggingface-cli download Stone-Chern/GSSC-S2D2-datasets \
    object_bank.tar.zst --repo-type dataset --local-dir .

zstd is required (apt install zstd, brew install zstd, or pip install zstandard).

Licence and attribution

Released under CC BY-NC-SA 4.0, inherited from SemanticKITTI, from which this corpus is derived. Any use must also cite the two papers 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.

The baseline predictions are outputs of SCPNet, JS3C-Net and LMSCNet; please cite those works when using the corresponding archives.

Reference code

github.com/BillyChern/GSSC-S2D2