---
title: ViGeo
canonical_url: "https://www.modelscope.cn/models/RunminZhang/ViGeo"
md_url: "https://www.modelscope.cn/models/RunminZhang/ViGeo.md"
repository: RunminZhang/ViGeo
last_updated: 2026-06-11
license: cc-by-nc-4.0
library_name:
  - pytorch
frameworks:
  - Pytorch
downloads: 2
stars: 0
tags:
  - video-depth-estimation
  - geometry-estimation
  - camera-pose-estimation
  - surface-normal-estimation
  - visual-geometry
  - vigeo
---

# ViGeo

> ViGeo - RunminZhang 在 ModelScope 开源的模型。ViGeo estimates scene geometry from either video clips or single-frame inputs, including depth, 3D points, surface normals, confidence, and camera poses for sequences.

RunminZhang/ViGeo 是 ModelScope 魔搭社区上的机器学习模型，采用 cc-by-nc-4.0 许可。

- **Repository**: RunminZhang/ViGeo
- **License**: cc-by-nc-4.0
- **Tags**: video-depth-estimation, geometry-estimation, camera-pose-estimation, surface-normal-estimation, visual-geometry, vigeo
- **Downloads**: 2
- **Stars**: 0
- **Last updated**: 2026-06-11

Source: https://www.modelscope.cn/models/RunminZhang/ViGeo

---

# ViGeo

ViGeo estimates scene geometry from either video clips or single-frame inputs,
including depth, 3D points, surface normals, confidence, and camera poses for
sequences.

The checkpoint in this repository is `vigeo.pt`.

## Checkpoint Note

This repository currently provides a preliminary ViGeo checkpoint. The current
checkpoint was trained with a known issue in the loss implementation, which may
cause minor visualization artifacts in camera poses and distant regions. This
checkpoint is consistent with the results reported in the paper and can be used
to obtain dense geometry estimation results.

We are preparing an updated checkpoint with a sky mask head and will release it
soon.

## Installation

```bash
conda create -n vigeo python=3.10 -y
conda activate vigeo

pip install torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1 --index-url https://download.pytorch.org/whl/cu126

git clone https://github.com/aigc3d/ViGeo.git
cd ViGeo
pip install -r requirements.txt
pip install -e .
```

## Quick Start

```python
import torch

from vigeo import ViGeo
from utils import load_image_sequence

device = torch.device("cuda")
image_paths = ["path/to/imageA.png", "path/to/imageB.png", "path/to/imageC.png"]
images = load_image_sequence(image_paths).to(device)  # [T, 3, H, W], RGB in [0, 1]

model = ViGeo.from_pretrained("pkqbajng/ViGeo").to(device).eval()

with torch.inference_mode():
    output = model.infer(images, mode="offline")

depth = output["depth_pred"]      # [T, 1, H, W]
points = output["points_pred"]    # [T, H, W, 3]
normals = output["normal_pred"]   # [T, H, W, 3], inward normals
normals_out = -normals            # outward normals for visualization/evaluation
poses = output["pose_pred"]       # [T, 3, 4], camera-to-world
confidence = output["conf_pred"]  # [T, 1, H, W]
```

For batched input `[B, T, 3, H, W]`, tensor outputs keep the leading batch
dimension.

ViGeo uses a right-handed camera coordinate system with `(X, Y, Z) = (right,
down, front)`. The raw `normal_pred` output follows the inward normal
convention. Use `normals = -normal_pred` when outward normals are needed for
visualization or evaluation.

## Inference Modes

ViGeo provides `offline`, `chunk`, and `online` inference modes. `offline`
processes the full input sequence at once and is preferred when the complete
video or image set is available. For long videos, use `chunk` or `online` mode
with cached context.

See the [ViGeo main branch README](https://github.com/aigc3d/ViGeo/tree/main#inference-modes)
for examples of all inference modes.

## Links

- ViGeo project page: https://pkqbajng.github.io/ViGeo/
- Paper: https://arxiv.org/abs/2605.30060
- GitHub repository: https://github.com/aigc3d/ViGeo
- Corrected paper PDF before the arXiv update is reflected: https://github.com/aigc3d/ViGeo/blob/main/assets/paper.pdf

## License

CC BY-NC 4.0.