README.md

metadata

pipeline_tag: image-to-3d
tags:
  - computer-vision
  - 3d-reconstruction
  - video-processing

LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory

This repository contains the reimplemented model checkpoints for LoGeR, as presented in the paper LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory.

LoGeR processes long video streams in chunks with a hybrid memory design to improve large-scale geometric reconstruction quality and consistency.

Checkpoints

We provide two main pre-trained models. To use them, please clone the corresponding GitHub repository and place the downloaded .pt files in your local ckpts/ directory as follows:

• ckpts/LoGeR/latest.pt
• ckpts/LoGeR_star/latest.pt

Download Commands

You can download the weights directly via wget:

# Download LoGeR
wget -O ckpts/LoGeR/latest.pt https://huggingface.co/Junyi42/LoGeR/resolve/main/LoGeR/latest.pt?download=true

# Download LoGeR_star
wget -O ckpts/LoGeR_star/latest.pt https://huggingface.co/Junyi42/LoGeR_star/latest.pt?download=true

Usage

For detailed instructions on installation, running demos, and evaluation, please refer to the main GitHub repository.

Citation

If you find our work or these models useful, please cite our paper:

@article{zhang2026loger,
  title={LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory},
  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},
  journal={arXiv preprint arXiv:2603.03269},
  year={2026}
}