<div align="center">
<h2>FastVGGT</h2>
</div>

## Overview

FastVGGT provides training-free acceleration for long-sequence 3D reconstruction with up to 4× faster inference while preserving accuracy.

## Environment Setup

```bash
conda create -n fastvggt python=3.10
conda activate fastvggt
pip install -r requirements.txt
```

## Observation

Note: A large number of input frames may slow down visualization. Try a smaller number first.

```bash
python eval/eval_scannet.py --input_frame 30 --vis_attn_map --merging 0
```

## Evaluation

### Custom Dataset

Directory layout:

```
<data_path>/
├── images/
│   ├── 000000.jpg
│   ├── 000001.jpg
│   └── ...
├── pose/          # Optional
└── gt_ply/        # Optional
```

- Required: images/
- When --enable_evaluation: pose/ and gt_ply/

Inference only:

```bash
python eval/eval_custom.py \
  --data_path /path/to/your_dataset \
  --output_path ./eval_results_custom \
  --plot
```

Inference + Evaluation:

```bash
python eval/eval_custom.py \
  --data_path /path/to/your_dataset \
  --enable_evaluation \
  --output_path ./eval_results_custom \
  --plot
```

COLMAP-format results:

```bash
python eval/eval_custom_colmap.py \
  --data_path /path/to/your_dataset \
  --output_path ./eval_results_custom_colmap
```

### ScanNet

```bash
python eval/eval_scannet.py --input_frame 1000 --merging 0
python eval/eval_scannet.py --input_frame 1000
```

### 7 Scenes & NRGBD

```bash
python eval/eval_7andN.py --kf 10
```
