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---
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tags:
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- computer-vision
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- 3d-reconstruction
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- video-processing
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---
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# LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory
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This repository contains the reimplemented model checkpoints for **LoGeR**.
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LoGeR processes long video streams in chunks with a hybrid memory design to improve large-scale geometric reconstruction quality and consistency.
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[](https://LoGeR-project.github.io/)
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[](https://github.com/Junyi42/LoGeR)
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[](https://arxiv.org/abs/2603.03269)
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<p align="center">
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<img src="https://loger-project.github.io/figs/fig1_teaser.png" alt="LoGeR Teaser" width="100%">
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</p>
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## Checkpoints
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We provide two main pre-trained models. To use them, please clone the [corresponding GitHub repository](https://github.com/Junyi42/LoGeR) and place the downloaded `.pt` files in your local `ckpts/` directory as follows:
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- `ckpts/LoGeR/latest.pt`
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- `ckpts/LoGeR_star/latest.pt`
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### Download Commands
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You can download the weights directly via `wget`:
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```bash
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# Download LoGeR
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wget -O ckpts/LoGeR/latest.pt https://huggingface.co/Junyi42/LoGeR/resolve/main/LoGeR/latest.pt?download=true
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# Download LoGeR_star
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wget -O ckpts/LoGeR_star/latest.pt https://huggingface.co/Junyi42/LoGeR/resolve/main/LoGeR_star/latest.pt?download=true
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```
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## Usage
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For detailed instructions on installation, running demos, and evaluation, please refer to the main [GitHub repository](https://github.com/Junyi42/LoGeR).
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## Citation
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If you find our work or these models useful, please cite our paper:
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```bibtex
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@article{zhang2026loger,
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title={LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory},
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author={Zhang, Junyi and Herrmann, Charles and Hur, Junhwa and Sun, Chen and Yang, Ming-Hsuan and Cole, Forrester and Darrell, Trevor and Sun, Deqing},
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journal={arXiv preprint arXiv:2603.03269},
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year={2026}
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}
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```
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