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- exp/litept/nuscenes/semseg-litept-v1m1-0-base/model/model_best.pth +3 -0
- exp/litept/scannet/semseg-litept-v1m1-0-base/model/model_best.pth +3 -0
- exp/litept/waymo/semseg-litept-v1m1-0-base/model/model_best.pth +3 -0
- exp/utonia/semseg-utonia-v1m1-0b-scannet-dec/model/model_best.pth +3 -0
- exp/utonia/semseg-utonia-v1m1-4b-nuscenes-dec/model/model_best.pth +3 -0
- exp/utonia/semseg-utonia-v1m1-5b-waymo-dec/model/model_best.pth +3 -0
README.md
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---
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license: other
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license_name: mixed-non-commercial
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license_link: https://github.com/YangDongHeon/SpaceCache#license
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library_name: pytorch
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pipeline_tag: other
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tags:
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- point-cloud
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- semantic-segmentation
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- lidar
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- streaming
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- efficient-inference
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- arxiv:2610.21207
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---
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# SpaceCache checkpoints
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Checkpoints for **SpaceCache: Intermediate Feature Reuse for Efficient Semantic
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Segmentation on Point Cloud Streams**
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([paper](https://arxiv.org/abs/2610.21207),
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[code](https://github.com/YangDongHeon/SpaceCache)).
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SpaceCache is training-free: these are the backbone segmentation models it
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runs on, Utonia and LitePT-B, trained on Waymo, nuScenes and ScanNet.
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## Usage
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From the root of the [SpaceCache repository](https://github.com/YangDongHeon/SpaceCache):
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```bash
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huggingface-cli download honeydong49/SpaceCache --local-dir .
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```
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The files land under `exp/`, where the evaluation configs read them.
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## Checkpoints
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| Backbone | Dataset | File |
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|---|---|---|
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| Utonia | Waymo | `exp/utonia/semseg-utonia-v1m1-5b-waymo-dec/model/model_best.pth` |
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| Utonia | nuScenes | `exp/utonia/semseg-utonia-v1m1-4b-nuscenes-dec/model/model_best.pth` |
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| Utonia | ScanNet | `exp/utonia/semseg-utonia-v1m1-0b-scannet-dec/model/model_best.pth` |
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| LitePT-B | Waymo | `exp/litept/waymo/semseg-litept-v1m1-0-base/model/model_best.pth` |
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| LitePT-B | nuScenes | `exp/litept/nuscenes/semseg-litept-v1m1-0-base/model/model_best.pth` |
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| LitePT-B | ScanNet | `exp/litept/scannet/semseg-litept-v1m1-0-base/model/model_best.pth` |
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Each file holds the model weights (`state_dict`) only. The Utonia models are
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a segmentation decoder trained on top of the released
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[Utonia](https://huggingface.co/Pointcept/Utonia) encoder, which was kept
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frozen; the file contains both. The LitePT-B models were trained from scratch
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with the [LitePT](https://github.com/prs-eth/LitePT) architecture.
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## License
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All checkpoints are for non-commercial research only.
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| Checkpoints | Terms |
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|---|---|
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| Utonia (all datasets) | contain the [Utonia](https://huggingface.co/Pointcept/Utonia) weights, CC BY-NC 4.0, plus the terms of the dataset below |
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| Waymo | [Waymo Dataset License Agreement for Non-Commercial Use](https://waymo.com/open/terms) (notice below) |
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| nuScenes | CC BY-NC-SA 4.0, [nuScenes terms of use](https://www.nuscenes.org/terms-of-use) |
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| ScanNet | [ScanNet Terms of Use](http://kaldir.vc.in.tum.de/scannet/ScanNet_TOS.pdf) |
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The checkpoints trained on Waymo were made using the Waymo Open Dataset,
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provided by Waymo LLC under the Waymo Dataset License Agreement for
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Non-Commercial Use, available at waymo.com/open/terms, and your access and use
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of such work are governed by the terms and conditions therein. They are
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released under that agreement; any further downstream use or modification of
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them is subject to its terms, including the non-commercial restrictions of its
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Section 4. They must not be used to operate or assist the operation of a
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vehicle, in production systems, or for other primarily commercial purposes.
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## Citation
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```bibtex
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@article{yang2026spacecache,
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title = {SpaceCache: Intermediate Feature Reuse for Efficient Semantic Segmentation on Point Cloud Streams},
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author = {Yang, Dongheon and Kang, Beomseok and Kim, Jae-Joon},
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journal = {arXiv preprint arXiv:2610.21207},
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year = {2026}
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}
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```
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exp/litept/scannet/semseg-litept-v1m1-0-base/model/model_best.pth
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exp/utonia/semseg-utonia-v1m1-0b-scannet-dec/model/model_best.pth
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