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README.md

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+---
+license: apache-2.0
+library_name: pytorch
+tags:
+- 3d
+- point-cloud
+- scannet
+- 3d-question-answering
+- 3d-dense-captioning
+---
+
+# PerLA: Perceptive 3D Language Assistant (CVPR 2025)
+
+Checkpoints for [PerLA](https://github.com/gfmei/PerLA), a 3D language assistant
+for question answering and dense captioning in indoor scenes.
+[Paper](https://arxiv.org/abs/2411.19774) · [Code](https://github.com/gfmei/PerLA) ·
+[Project page](https://gfmei.github.io/PerLA/)
+
+Each checkpoint holds the trained PerLA weights (detector, merger, Q-Former,
+prompt encoder and projections) and its model settings. The frozen OPT-1.3B
+language model is loaded separately from `facebook/opt-1.3b`.
+
+| File | Benchmark | CIDEr | BLEU-4 | METEOR | ROUGE-L |
+| --- | --- | ---: | ---: | ---: | ---: |
+| `perla_scanqa.pth` | ScanQA val | 77.06 | 13.91 | 15.46 | 37.00 |
+| `perla_scanrefer.pth` | ScanRefer val, IoU 0.5 | 62.16 | 34.37 | 25.51 | 53.66 |
+| `perla_nr3d.pth` | Nr3D val, IoU 0.5 | coming soon | | | |
+
+All models are trained per task from the pretrained detector, without
+generalist pretraining (single run, seed 0; best validation epoch).
+
+## Usage
+
+```bash
+git clone https://github.com/gfmei/PerLA.git && cd PerLA
+hf download RyanMei/PerLA perla_scanqa.pth --local-dir ckpts
+bash scripts/eval.sh scanqa ckpts/perla_scanqa.pth
+```
+
+See the [code repository](https://github.com/gfmei/PerLA) for installation,
+data preparation and training.
+
+## Citation
+
+```bibtex
+@inproceedings{mei2025PerLA,
+  title     = {PerLA: Perceptive 3D Language Assistant},
+  author    = {Guofeng Mei and Wei Lin and Luigi Riz and Yujiao Wu and Fabio Poiesi and Yiming Wang},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year      = {2025}
+}
+```