MAPPE

Purpose

MAPPE (Mesh Assessment via Point Cloud and Projection Estimators) is a full-reference mesh quality assessment framework. It combines image quality features from rendered views with PointPCA3 features from sampled point clouds. This repository is the entry point for the MAPPE experiments and regression-based evaluation.

The Python distribution and import are both named mappe.

Related projects

Requirements

• Python 3.11 or 3.12 and uv.
• Git to clone the repository and network access to download Python packages.
• Mesh datasets, including OBJ files and any associated MTL files and texture images.

The package requires Python >=3.11,<3.13; its mesh-projector dependency uses enum.StrEnum, which requires Python 3.11 or later. A GPU is optional for the default CPU mesh renderer and CPU PyTorch execution. GPU execution requires compatible hardware and drivers.

Installation

Clone MAPPE and install the default environment:

git clone https://github.com/arthurhscarvalho/mappe.git mappe
cd mappe
uv sync --python 3.12

MAPPE installs mesh-projector==1.0.0 and pointpca3==1.0.0 from PyPI. Their published wheels include the Rust extensions, so supported platforms require no Rust toolchain or sibling repository checkouts. pointpca2 is also resolved from PyPI.

For the configured AMD ROCm environment, use:

uv sync --python 3.12 --no-default-groups --extra rocm

This selects the ROCm 6.3 package index configured in pyproject.toml. Use a compatible ROCm installation and GPU. Run subsequent uv run commands with --no-default-groups --extra rocm as well, for example uv run --no-default-groups --extra rocm python scripts/main_experiment.py.

Usage

from math import pi

import mappe
import mesh_projector as mp

light_config = mp.PointLights(
    positions=[(-1.0, 1.0, 1.0), (1.0, 1.0, -1.0)],
    global_ambient=(0.6, 0.6, 0.6),
    global_diffuse=(0.6, 0.6, 0.6),
    global_specular=(0.0, 0.0, 0.0),
)
mqa_config = mappe.MQAConfig(
    search_size=361,
    render_config=mp.RenderConfig(resolution=512, ignore_opacity=True),
    iqa_projection_config=mp.ImageProjectionConfig(light_config=light_config),
    pcqa_projection_config=mp.PointCloudProjectionConfig(
        density_factor=0.3,
        light_config=light_config,
    ),
    view_angles=[
        (0.0, 0.0, 0.0),
        (0.0, pi, 0.0),
        (0.0, pi / 2, 0.0),
        (0.0, -pi / 2, 0.0),
        (pi / 2, 0.0, 0.0),
        (-pi / 2, 0.0, 0.0),
    ],
)

features = mappe.process_mqa(
    "reference.obj",
    "distorted.obj",
    mqa_config=mqa_config,
    device="cpu",
    max_workers=8,
)
print(features)

Experiments

The scripts/ directory contains dataset experiments, ablation studies, parameter searches, and regression evaluations. Dataset-specific configurations are in scripts/dataset_configs.py, and metadata CSVs are in data/. Configure dataset paths and experiment parameters before running these scripts.

Publications

1. Arthur Henrique Silva Carvalho and Pedro Garcia Freitas. “MAPPE: Mesh Assessment via Point Cloud and Projection Estimators.” Signal Processing: Image Communication, vol. 150, article 117722, 2027. DOI: 10.1016/j.image.2026.117722. Available online 5 October 2026.

@article{carvalho2027mappe,
  author  = {Carvalho, Arthur Henrique Silva and Freitas, Pedro Garcia},
  title   = {{MAPPE}: Mesh Assessment via Point Cloud and Projection Estimators},
  journal = {Signal Processing: Image Communication},
  volume  = {150},
  pages   = {117722},
  year    = {2027},
  doi     = {10.1016/j.image.2026.117722}
}

License

MIT License.