| --- |
| license: apache-2.0 |
| base_model: Qwen/Qwen2.5-VL-3B-Instruct |
| base_model_relation: finetune |
| library_name: transformers |
| pipeline_tag: image-text-to-text |
| language: |
| - en |
| datasets: |
| - MLL-Lab/MindCube |
| tags: |
| - multi-view |
| - spatial-reasoning |
| - 3d-reasoning |
| - visual-question-answering |
| - cognitive-map |
| --- |
| |
| # DR-MV3D (SFT) |
|
|
| Official checkpoint for **[Dense Reward for Multi-View 3D Reasoning with Global Maps and Local Views](https://arxiv.org/abs/2606.23557)** (ECCV 2026). |
|
|
| [Paper](https://arxiv.org/abs/2606.23557) 路 [Project page](https://dr-mv3d.github.io/) 路 [Code](https://github.com/kaist-cvml/DR-MV3D) 路 [SFT + GRPO checkpoint](https://huggingface.co/jihochoi/DR-MV3D-R-GRPO) |
|
|
| DR-MV3D answers questions about a 3D scene shown from several viewpoints. This is the supervised fine-tuning stage: [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) trained on map-grounded reasoning traces. For general use prefer the [final SFT + GRPO checkpoint](https://huggingface.co/jihochoi/DR-MV3D-R-GRPO). |
|
|
| ## Usage |
|
|
| The model expects the structured task prompt it was trained with, and produces a cognitive map, per-view ego-centric maps, a reasoning chain and the answer. See the [GitHub repository](https://github.com/kaist-cvml/DR-MV3D) for the prompt format, data preparation, inference and evaluation. |
|
|
| ```bash |
| python scripts/run_inference.py \ |
| --model-path jihochoi/DR-MV3D-R-SFT \ |
| -i data/prompts/MindCube_tinybench_drmv3d.jsonl \ |
| -o results/tinybench_responses.jsonl \ |
| --image-root data --batch-size 8 |
| ``` |
|
|
| ## License |
|
|
| The weights are a fine-tune of Qwen2.5-VL-3B-Instruct and are released under Apache-2.0, the base model's license. The code is MIT-licensed. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{drmv3d2026, |
| title={Dense Reward for Multi-View 3D Reasoning with Global Maps and Local Views}, |
| author={Choi, Jiho and Lee, Seonho and Park, Seojeong and Shim, Hyunjung}, |
| booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, |
| year={2026}, |
| eprint={2606.23557}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV} |
| } |
| ``` |
|
|
