# FLARE

**Repository Path**: Hiwen/FLARE

## Basic Information

- **Project Name**: FLARE
- **Description**: https://github.com/ant-research/FLARE
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-21
- **Last Updated**: 2025-02-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views
[![Website](https://img.shields.io/website-up-down-green-red/http/shields.io.svg)](https://zhanghe3z.github.io/FLARE/)
[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97-Hugging%20Face-yellow)](https://huggingface.co/AntResearch/FLARE)
[![Video](https://img.shields.io/badge/Video-Demo-red)](https://zhanghe3z.github.io/FLARE/videos/teaser_video.mp4)

Official implementation of **FLARE** (arXiv 2025) - a feed-forward model for joint camera pose estimation, 3D reconstruction and novel view synthesis from sparse uncalibrated views.

![Teaser Video](./assets/teaser.jpg)


<!-- TOC start (generated with https://github.com/derlin/bitdowntoc) -->

- [📖 Overview](#-overview)
- [🛠️ TODO List](#-todo-list)
- [🌍 Installation](#-installation)
- [💿 Checkpoints](#-checkpoints)
- [🎯 Run a Demo (Point Cloud and Camera Pose Estimation) ](#-run-a-demo-point-cloud-and-camera-pose-estimation)
- [👀 Visualization ](#-visualization)
- [📜 Citation ](#-citation)

<!-- TOC end -->

## 📖 Overview
We present FLARE, a feed-forward model that simultaneously estimates high-quality camera poses, 3D geometry, and appearance from as few as 2-8 uncalibrated images. Our cascaded learning paradigm:

1. **Camera Pose Estimation**: Directly regress camera poses without bundle adjustment
2. **Geometry Reconstruction**: Decompose geometry reconstruction into two simpler sub-problems
3. **Appearance Modeling**: Enable photorealistic novel view synthesis via 3D Gaussians

Achieves SOTA performance with inference times <0.5 seconds!

## 🛠️ TODO List
- [x] Release point cloud and camera pose estimation code.
- [ ] Release novel view synthesis code. (~2 weeks)
- [ ] Release evaluation code. (~2 weeks)
- [ ] Release training code.
- [ ] Release data processing code.

## 🌍 Installation

```
conda create -n flare python=3.8
conda activate flare 
conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia  # use the correct version of cuda for your system
pip install -r requirements.txt
conda uninstall ffmpeg  
conda install -c conda-forge ffmpeg
```


## 💿 Checkpoints
Download the checkpoint from [huggingface](https://huggingface.co/AntResearch/FLARE/blob/main/geometry_pose.pth) and place it in the /checkpoints/geometry_pose.pth directory.

## 🎯 Run a Demo (Point Cloud and Camera Pose Estimation) 


```
sh run_pose_pointcloud.sh
```


```
torchrun --nproc_per_node=1 run_pose_pointcloud.py \
    --test_dataset "1 @ CustomDataset(split='train', ROOT='Your/Data/Path', resolution=(512,384), seed=1, num_views=8, gt_num_image=0, aug_portrait_or_landscape=False, sequential_input=False)" \
    --model "AsymmetricMASt3R(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='catmlp+dpt', output_mode='pts3d+desc24', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12, two_confs=True, desc_conf_mode=('exp', 0, inf))" \
    --pretrained "Your/Checkpoint/Path" \
    --test_criterion "MeshOutput(sam=False)" --output_dir "log/" --amp 1 --seed 1 --num_workers 0
```

## 👀 Visualization 

```
sh ./visualizer/vis.sh
```
 

```
CUDA_VISIBLE_DEVICES=0 python visualizer/run_vis.py --result_npz data/mesh/IMG_1511.HEIC.JPG.JPG/pred.npz --results_folder data/mesh/IMG_1511.HEIC.JPG.JPG/
``` 


## 📜 Citation 
```bibtex
@misc{zhang2025flarefeedforwardgeometryappearance,
      title={FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views}, 
      author={Shangzhan Zhang and Jianyuan Wang and Yinghao Xu and Nan Xue and Christian Rupprecht and Xiaowei Zhou and Yujun Shen and Gordon Wetzstein},
      year={2025},
      eprint={2502.12138},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.12138}, 
}