Indoor Path Planning App (Open3D)

Open3D GUI for indoor path planning on 3D meshes / point clouds. Originally developed as part of an M.Sc. course project at the VVR group.

Demo

Plane detection/removal on mesh (RANSAC)

Sampling and plane detection/removal on point cloud (RANSAC)

Clustering (DBSCAN)

Door candidate detection

2D occupancy grid creation

Path planning (A*)

Overview

This app provides an interactive pipeline for indoor navigation on 3D meshes/point clouds. It supports mesh sampling, plane detection/removal, clustering-based obstacle extraction, 2D grid projection and obstacle expansion, and A* path planning, all inside an Open3D GUI.

Features

• Mesh to point cloud sampling
• Plane detection/removal (RANSAC) on mesh or point cloud
• DBSCAN clustering for obstacle segmentation
• Door candidate detection (simple geometric heuristic)
• 2D occupancy grid projection + obstacle expansion (radius slider)
• A* path planning with start/goal selection on the grid
• Interactive visualization (mesh/wireframe, point cloud coloring, 2D grid view, 3D path overlay)

Installation

Create and activate a Python environment (recommended), then install dependencies:

pip install -r requirements.txt

Tested with Python 3.12.12 on Windows.

Usage

Run the app with a .ply mesh:

python app.py path/to/mesh.ply

Demo data

The GIFs in this README were generated using a scene from the Stanford Large-Scale 3D Indoor Spaces dataset (S3DIS). The dataset files are not included in this repository. A public mirror of S3DIS is available here.

If you use S3DIS in academic work, please cite: I. Armeni et al., "3D Semantic Parsing of Large-Scale Indoor Spaces", CVPR 2016.

License

MIT License (see LICENSE).