PerLA: Perceptive 3D Language Assistant (CVPR 2025)

Checkpoints for PerLA, a 3D language assistant for question answering and dense captioning in indoor scenes. Paper · Code · Project page

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

The model is trained on ScanQA from the pretrained detector, without generalist pretraining (single run, seed 0; best validation epoch).

Usage

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 for installation, data preparation and training.

Citation

@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}
}