| license: mit | |
| pipeline_tag: image-feature-extraction | |
| # UniPR-3D: Towards Universal Visual Place Recognition with Visual Geometry Grounded Transformer | |
| UniPR-3D is a universal visual place recognition (VPR) framework that effectively integrates information from multiple views. It supports both frame-to-frame and sequence-to-sequence matching by leveraging 3D and 2D tokens with tailored aggregation strategies. | |
| - **Paper:** [UniPR-3D: Towards Universal Visual Place Recognition with Visual Geometry Grounded Transformer](https://huggingface.co/papers/2512.21078) | |
| - **Repository:** [https://github.com/dtc111111/UniPR-3D](https://github.com/dtc111111/UniPR-3D) | |
| ## Description | |
| UniPR-3D builds on a Visual Geometry Grounded Transformer (VGGT) backbone capable of encoding multi-view 3D representations. To construct its descriptor, the model jointly leverages 3D tokens and intermediate 2D tokens, using dedicated aggregation modules to capture fine-grained texture cues while reasoning across viewpoints. To further enhance generalization, it incorporates both single- and multi-frame aggregation schemes along with a variable-length sequence retrieval strategy. It achieves state-of-the-art performance on several benchmarks, including MSLS, Pittsburgh, NordLand, and SPED. | |
| ## Citation | |
| If you find our paper and code useful, please cite us: | |
| ```bibtex | |
| @inproceedings{deng2026_unipr3d, | |
| title = {UniPR-3D: Towards Universal Visual Place Recognition with 3D Visual Geometry Grounded Transformer}, | |
| author = {Tianchen Deng and Xun Chen and Ziming Li and Hongming Shen and Danwei Wang and Javier Civera and Hesheng Wang}, | |
| booktitle = {Arxiv}, | |
| year = {2026}, | |
| } | |
| ``` |