Functional Classification of Point Clouds

Overview

This repository contains code to generate functional classification of observed point clouds in IsaacLab. This means classifying each point in the point cloud into either functional or non-functional areas.

Intallation

• Install Isaac Lab by following the installation guide.
• Clone this repo:

git clone https://github.com/BE2R-Lab-RND-AI-Grasping/gt_functional_pc.git
cd gt_functional_pc

• Install extra python dependencies: python -m pip install -r requirements.txt

• Download and extract dataset from here

Usage

• Run the script as follows, using any object/model from the dataset instead of fixed_joint_pliers/model_0

python demo.py  --mesh_path dataset/fixed_joint_pliers/model_0/object_convex_decomposition.obj --gt_pc_path dataset/fixed_joint_pliers/model_0/point_cloud_labeled.ply --device cuda --scale 0.001 --enable_cameras --visualize_pc

Examples

Functional areas are labeled in red, and non-functional areas in blue.

drill.mp4

pliers.mp4