| --- |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - lidar |
| - semantic-scene-completion |
| - semantickitti |
| - autonomous-driving |
| --- |
| |
| # 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`](https://huggingface.co/datasets/Stone-Chern/PS3-SemanticKITTI), |
| > mirroring IEEE DataPort [`10.21227/nqgf-9k39`](https://dx.doi.org/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 |
|
|
| ```bash |
| # 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. |
|
|
| ## Paper |
|
|
| Generative Semantic Scene Completion — https://arxiv.org/abs/2608.26737 |
|
|
| ## Reference code |
|
|
| [github.com/BillyChern/GSSC-S2D2](https://github.com/BillyChern/GSSC-S2D2) |
|
|
