🌊 SurGe: Improved Surface Geometry in Point Maps

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

📢 News

• 2026-06-01: arXiv v1, inference code, weights and demo.

Installation

uv sync --extra cli --extra app

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.

Tip

If you want the exact model from the arXiv v1 paper, you can git checkout v1 and specify corresponding checkpoint version SurGe.from_pretrained("karimknaebel/surge-large", revision="v1").

import torch
from surge import SurGe

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

result = model.infer(image, num_tokens="max")
points = result["points"]          # (B, H, W, 3)
depth = result["depth"]            # (B, H, W)
intrinsics = result["intrinsics"]  # (B, 3, 3)

num_tokens controls the encoder token budget. Use "min", "max", or an integer value.

CLI

Run inference on an image or a directory of images:

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

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

uv run --extra cli surge-cli 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 --extra app surge-app

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

Output Conventions

Coordinate frames:

• point_map.exr and point_normals_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:

• point_map.exr: float32 XYZ points. Masked pixels are NaN.
• point_normals_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.

Neighborhood Attention Decoder (NAD) Module

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

⚖️ License

The SurGe code is released under the MIT license, except for the DINOv2 code in src/surge/_vendor/dinov2 which is released by Meta AI under the Apache 2.0 license. The SurGe weights are released under CC BY-NC 4.0, due to the training datasets used.

🎓 Citation

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

@article{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}
    journal = {arXiv preprint arXiv:2605.31577},
}