Surf-LEAF

| Project Report |

Surf-LEAF (Surface-Level Extraction with Advancing Front Reconstruction Meshing) is a custom mesh extraction pipeline tailored for Gaussian Splatting reconstructions in urban environments.

Gaussian Splatting is a recent point-based scene representation that enables real-time, high-fidelity novel view synthesis across large-scale scenes. While it excels in rendering, extracting high-quality meshes from Gaussian reconstructions remains a challenge—especially in urban scenes where sharp edges, low-contrast surfaces, and sparse regions can degrade mesh fidelity.

We demonstrate improved mesh quality over existing techniques like DN-Splatter, achieving a 10% reduction in Chamfer distance on the TartanAir dataset.

Table of Contents

• Installation
• Usage
• Example

Installation

Prerequisites:

• A working installation of Nerfstudio
• QED-Splatter or DN-Splatter for training Gaussian Splatting reconstructions.

1. Clone the repository and its submodules by running:

git clone https://github.com/Soldann/surf-leaf.git
cd surf-leaf

2. Load your nerfstudio conda environment:

conda activate nerfstudio

3. Install the package and its dependencies:

pip install -e .

Usage

The surf_leaf package provides a command line interface to run the mesh extraction pipeline. The basic usage is as follows:

surf-leaf --load_config /<Path to your run>/config.yml

This will load the Gaussian Splatting reconstruction from the specified path and run the mesh extraction pipeline with default parameters.

A full table of parameters:

Parameter	Type	Default	Description
load_config	Path	REQUIRED	Path to the trained config YAML file.
output_dir	Path	"./mesh_exports/"	Path to the output directory.
total_points	int	2_000_000	Total number of surface samples to extract.
use_masks	bool	False	If dataset has masks, use them to restrict surface sampling.
surface_levels	List[float]	[0.3]	Surface level isosurfaces to extract.
return_normal	"analytical", "closest_gaussian", "average"	"closest_gaussian"	Strategy for estimating normals from Gaussians.
cropbox_pos	Optional[Tuple[float, float, float]]	None	Position of the cropbox center (x, y, z).
cropbox_rpy	Optional[Tuple[float, float, float]]	None	Orientation of cropbox in roll, pitch, yaw (radians).
cropbox_scale	Optional[Tuple[float, float, float]]	None	Scale (size) of the cropbox.
voxel_size	float	0.4	Voxel size used during meshing.
margin_seam	float	0.04	Seam margin used during meshing.
margin_discard	float	0.04	Discard margin used during meshing.
max_edge_length	float	1.0	Maximum triangle edge length in the mesh.
postprocess_alpha_fraction	float	0.001	Alpha wrapping ball size fraction.
postprocess_stepsmoothnum	int	1	Number of HC Laplacian smoothing steps.
postprocess_targetperc	float	0.6	Target reduction percentage for mesh simplification.

Example

For our example scene we use the TartanAir dataset, specifically the abandoned_factory/P001 scene. We use the Tartan to Nerfstudio Converter to convert the dataset to a Nerfstudio-compatible format. This includes camera poses, images, and depth images.

We use QED-Splatter to train a Gaussian Splatting reconstruction of the scene.

After training, we can extract the mesh using Surf-LEAF:

surf-leaf --load_config /outputs/qed-splatter/2025-06-23_022015/abandoned_factory/P001/config.yml \
          --cropbox_scale 2.0 2.0 2.0

This will extract the mesh from the Gaussian Splatting reconstruction, using a cropbox scale of 2.0 in all dimensions. The resulting mesh will be saved in the mesh_exports directory, with the name based on the scene and timestamp.