# STDLoc **Repository Path**: junhao_wei/stdloc ## Basic Information - **Project Name**: STDLoc - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-09-25 - **Last Updated**: 2025-12-10 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

From Sparse to Dense: Camera Relocalization with Scene-Specific Detector from Feature Gaussian Splatting

Zhiwei Huang1,2  Hailin Yu2†  Yichun Shentu2  Jin Yuan2  Guofeng Zhang1† 
1State Key Lab of CAD & CG, Zhejiang University 2SenseTime Research
† Corresponding Authors
CVPR2025

## 🏠 About
Dialogue_Teaser
This paper presents a novel camera relocalization method, STDLoc, which leverages Feature GS as scene representation. STDLoc is a full relocalization pipeline that can achieve accurate relocalization without relying on any pose prior. Unlike previous coarse-to-fine localization methods that require image retrieval first and then feature matching, we propose a novel sparse-to-dense localization paradigm. Based on this scene representation, we introduce a novel matching-oriented Gaussian sampling strategy and a scene-specific detector to achieve efficient and robust initial pose estimation. Furthermore, based on the initial localization results, we align the query feature map to the Gaussian feature field by dense feature matching to enable accurate localization. The experiments on indoor and outdoor datasets show that STDLoc outperforms current state-of-the-art localization methods in terms of localization accuracy and recall. ## πŸ” Performance The code in this repository has a better performance than our paper, through some adjustments: 1. Set `align_corners=False` in interpolation. 2. Use a smaller learning rate for ourdoor dataset. 3. Use the anti-aliasing feature of gsplat. ### 7-Scenes | Method | Chess | Fire | Heads | Office | Pumpkin | RedKitchen | Stairs | Avg.↓[cm/β—¦] | | -------------- | --------- | --------- | --------- | --------- | --------- | ---------- | --------- | ----------- | | STDLoc (paper) | 0.46/0.15 | 0.57/0.24 | 0.45/0.26 | 0.86/0.24 | 0.93/0.21 | 0.63/0.19 | 1.42/0.41 | 0.76/0.24 | | STDLoc (repo) | 0.43/0.13 | 0.49/0.20 | 0.41/0.24 | 0.72/0.21 | 0.91/0.23 | 0.59/0.14 | 1.19/0.36 | 0.67/0.22 | ### Cambridge Landmarks | Method | Court | King’s | Hospital | Shop | St. Mary’s | Avg.↓[cm/β—¦] | | -------------- | --------- | --------- | --------- | -------- | ---------- | ----------- | | STDLoc (paper) | 15.7/0.06 | 15.0/0.17 | 11.9/0.21 | 3.0/0.13 | 4.7/0.14 | 10.1/0.14 | | STDLoc (repo) | 11.5/0.06 | 14.7/0.15 | 11.3/0.21 | 2.5/0.12 | 3.5/0.12 | 8.7/0.13 | ## πŸ“¦ Training and Evaluation ### Environment Setup 1. Clone this repository. ```bash git clone --recursive https://github.com/zju3dv/STDLoc.git ``` 2. Install packages ```bash conda create -n stdloc python=3.8 -y pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124 pip install -r requirements.txt # install submodules pip install submodules/simple-knn pip install submodules/gsplat ``` ### Data Preparation We use two public datasets: - [Microsoft 7-Scenes](https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/) - [Cambridge Landmarks](https://www.repository.cam.ac.uk/handle/1810/251342/) #### 7-Scenes Dataset 1. Download images following HLoc. ```bash export dataset=datasets/7scenes for scene in chess fire heads office pumpkin redkitchen stairs; \ do wget http://download.microsoft.com/download/2/8/5/28564B23-0828-408F-8631-23B1EFF1DAC8/$scene.zip -P $dataset \ && unzip $dataset/$scene.zip -d $dataset && unzip $dataset/$scene/'*.zip' -d $dataset/$scene; done ``` 2. Download full reconstructions from [visloc_pseudo_gt_limitations](https://github.com/tsattler/visloc_pseudo_gt_limitations/tree/main?tab=readme-ov-file#full-reconstructions): ```bash pip install gdown gdown 1ATijcGCgK84NKB4Mho4_T-P7x8LSL80m -O $dataset/7scenes_reference_models.zip unzip $dataset/7scenes_reference_models.zip -d $dataset # move sfm_gt to each dataset for scene in chess fire heads office pumpkin redkitchen stairs; \ do mkdir -p $dataset/$scene/sparse && cp -r $dataset/7scenes_reference_models/$scene/sfm_gt $dataset/$scene/sparse/0 ; done ``` #### Cambridge Landmarks Dataset 1. Download images from PoseNet's project page: ```bash export dataset=datasets/cambridge export scenes=( "KingsCollege" "OldHospital" "StMarysChurch" "ShopFacade" "GreatCourt" ) export IDs=( "251342" "251340" "251294" "251336" "251291" ) for i in "${!scenes[@]}"; do wget https://www.repository.cam.ac.uk/bitstream/handle/1810/${IDs[i]}/${scenes[i]}.zip -P $dataset \ && unzip $dataset/${scenes[i]}.zip -d $dataset ; done ``` 2. Install Mask2Former: ```bash cd submodules/Mask2Former pip install -r requirements.txt python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' cd mask2former/modeling/pixel_decoder/ops sh make.sh cd ../../../.. # download model wget https://dl.fbaipublicfiles.com/maskformer/mask2former/coco/panoptic/maskformer2_swin_large_IN21k_384_bs16_100ep/model_final_f07440.pkl cd ../.. ``` 3. Preprocess data: ```bash bash scripts/dataset_preprocess.sh ``` ### Training Feature Gaussian ```bash # For 7-Scenes: bash scripts/train_7scenes.sh # For Cambridge Landmarks bash scripts/train_cambridge.sh ``` ### Evaluation We also provide pretrained models for [7-Scenes](https://drive.google.com/file/d/1gxmmpYD-XjYT01cu0flfNHf0CuJDIXsh/view) and [Cambridge Landmarks](https://drive.google.com/file/d/1EbKx9NY2cgtIxkQQ7Spjpl90PG68GKgu/view) datasets here. ```bash gdown 1gxmmpYD-XjYT01cu0flfNHf0CuJDIXsh gdown 1EbKx9NY2cgtIxkQQ7Spjpl90PG68GKgu unzip map_7scenes.zip unzip map_cambridge.zip ``` Reproduce the experimental results. ```bash # For 7-Scenes: bash scripts/evaluate_7scenes.sh # For Cambridge Landmarks bash scripts/evaluate_cambridge.sh ``` ## πŸ”— Citation ```bibtex @inproceedings{huang2025stdloc, title={From Sparse to Dense: Camera Relocalization with Scene-Specific Detector from {Feature Gaussian Splatting}}, author={Huang, Zhiwei and Yu, Hailin and Shentu, Yichun and Yuan, Jin and Zhang, Guofeng}, booktitle={CVPR}, pages={}, year={2025} } ``` ## πŸ‘ Acknowledgements - [Feature 3DGS](https://github.com/ShijieZhou-UCLA/feature-3dgs): Our codebase is built upon Feature 3DGS. - [gsplat](https://github.com/nerfstudio-project/gsplat): We use gsplat as our rasterization backend.