🌊 SurGe: Improved Surface Geometry in Point Maps

[Paper] [arXiv] [Project Page] [Weights] [Demo] [BibTeX]

📢 News

• 2026-09-25: Accepted to NeurIPS 2026.
• 2026-08-21: Evaluation code released.
• 2026-06-01: arXiv v1, inference code, weights, and demo released.

Installation

From a local clone

Clone the repository:

git clone https://github.com/karimknaebel/surge
cd surge

Then install SurGe with the Inference CLI, Gradio app, and evaluation dependencies:

uv sync --all-extras

This creates a virtual environment in .venv/. Run commands with uv run (e.g., uv run surge-infer ...), or just activate it as usual with source .venv/bin/activate.

Alternatively, with pip: pip install -e ".[cli,app,eval]"

As a library

Use SurGe in an existing project:

uv add git+https://github.com/karimknaebel/surge

Alternatively, with pip: pip install git+https://github.com/karimknaebel/surge

Usage

Python API

SurGe expects image tensors in BCHW format with unnormalized RGB values in [0, 1]. Do not apply ImageNet normalization or similar preprocessing.

import torch
from surge import SurGe

model = SurGe.from_pretrained("karimknaebel/surge-large").eval().cuda()
image = torch.rand(1, 3, 518, 518, device="cuda")

with torch.autocast("cuda", dtype=torch.float16):
    result = model.infer(image)
points = result["points"]          # (B, H, W, 3)
depth = result["depth"]            # (B, H, W)
intrinsics = result["intrinsics"]  # (B, 3, 3)

🚀 Inference CLI

Run inference on an image or a directory of images:

uv run surge-infer path/to/image.jpg --output-dir output

By default, the Inference CLI writes mesh.glb for each input image. Add output flags as needed:

uv run surge-infer path/to/images --save-maps --save-glb --save-ply

Useful options include --max-size 1200, --tokens max, --fov-x 60, --fp16, and --filter-sky. For interactive viewing, use --show-mesh to open the reconstructed mesh with trimesh, or --rerun to log the inference results to a Rerun viewer.

🖥️ Gradio App

Launch the local demo app:

uv run surge-app

The app lets you upload an image, adjust the token budget and mesh cleanup settings, view the reconstructed mesh, and download the generated maps and geometry.

📊 Evaluation

Download MoGe's processed evaluation datasets from Hugging Face and extract them under data/eval:

mkdir -p data/eval
uv run hf download Ruicheng/monocular-geometry-evaluation \
  --repo-type dataset \
  --local-dir data/eval

cd data/eval
unzip '*.zip'
cd ../..

Run the full suite:

uv run surge-eval --output eval_output/surge.json

We include official surge-large results for reference.

Output Conventions

Coordinate frames:

• point_map.exr and point_normal_map.png: RDF (OpenCV); +X right, +Y down, +Z forward.
• mesh.glb and point_cloud.ply: RUB; +X right, +Y up, +Z backward.

File formats:

• depth_colorized.png: colorized depth visualization.
• point_map.exr: float32 XYZ points. Masked pixels are NaN.
• point_normal_map.png: unit normals. RGB stores [X, -Y, -Z] mapped from [-1, 1] to [0, 255]; invalid pixels are [127, 127, 127]. Renormalize after decoding if needed.
• intrinsics.json: camera intrinsics as a 3×3 JSON array.
• fov.json: horizontal and vertical fields of view in degrees.

🧩 Neighborhood Attention Decoder (NAD) Module

The NAD is implemented as a reusable PyTorch module. It is intentionally self-contained, so you can copy it into your project as a single file without pulling in the rest of SurGe.

🧩 Point Gradient Matching ($\mathcal{L}_{\mathrm{pgm}}$)

The self-contained point gradient matching loss used to train SurGe is included for reference.

🧩 Point Map Normal Mean Angular Error ($\mathrm{MAE}_{\mathrm{normal}}$)

The normal mean angular error metric used to evaluate surface normals is included in the evaluation code.

⚖️ License

The SurGe code is released under the MIT license. The SurGe weights are released under CC BY-NC 4.0, due to the training datasets used.

🙏 Acknowledgments

We thank the MoGe project for their open-source code.

🎓 Citation

If you use our work in your research, please use the following BibTeX entry.

@inproceedings{knaebel2026surge,
    title     = {{SurGe}: Improved Surface Geometry in Point Maps},
    author    = {Knaebel, Karim and Martin Garcia, Gonzalo and Schmidt, Christian and Fradlin, Ilya and Nunes, Lucas and de Geus, Daan and Leibe, Bastian},
    year      = 2026,
    booktitle = {Advances in Neural Information Processing Systems},
}