Inverse Farthest Point Sampling (IFPS)

This repository is the official implementation of our paper:

Inverse Farthest Point Sampling (IFPS): A Universal and Hierarchical Shell Representation for Discrete Data

Links: [Video(Youtube)]

Method

Core idea in one sentence. Using only the first N sampled points, the IFPS shell is constructed to encapsulate all the original discrete points while employing hierarchical management.

Pipeline

Implicit Sphere Tree and IFPS Shells with Growing Point Numbers

Multi-way Tree Structure and IFPS Shells under Various Norms

Setup

Python 3 dependencies:

• numba 0.58.1
• trimesh 4.1.4
• h5py 3.10.0

conda create -n Ifps python=3.8
conda activate Ifps
conda install pytorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 pytorch-cuda=11.8 -c pytorch -c nvidia
pip install .

DataSet

You can use the example dataset in IFPS/dataset/thingi32_normalization/ or you can put your custom datasets in IFPS/dataset/ directory.

Configurations

All configurable settings are accessible within the IFPS/utils/options.py

Run

python example.py