Kanma/ppsdf

Code examples for "Online learning of Continuous Signed Distance Fields Using Piecewise Polynomials"

ppSDF

Code examples for Online learning of Continuous Signed Distance Fields Using Piecewise Polynomials.

This repository started as a fork of the RDF codebase.

Dependencies

Tested with Python 3.10.12 on Ubuntu 22.04 and macOS 14.4.1.

Python dependencies are listed in requirements.txt. Open3D additionally requires a working C++ compiler.

(Optional) For GPU acceleration, a compatible version of CUDA is required (tested with CUDA 11.5).

Conda setup

Setup a conda new environment:

conda create --name ppSDF python=3.10.12

Activate the environment:

conda activate ppSDF

Install the required packages:

pip install -r requirements.txt

Downloading the YCB dataset

The desired objects from the YCB dataset (Calli et al.) can be downloaded by running

python ycb_downloader.py

Objects can be selected by commenting/uncommenting elements in the objects_to_download list inside the script.

Running the example script

To run the example script with the desired arguments:

• n_seg - number of segments per input dimension
• qd, qn, qt - cost coefficients for incremental learning
• sigma - measurement noise
• batch_size - batch size for the incremental updates
• device - device to run on (e.g., cuda or cpu)
• save - if True, saves the approximated SDF
• object - which object to load
• n_data - number of training sample points
• cut_x, cut_y, cut_z - select axis to cut for visualization

For example, to approximate the '035_power_drill' object with 5 segments per input dimension and 1000 training samples:

python reconstruct.py --object=035_power_drill --n_seg=5 --n_data=1000

To run with default arguments:

python reconstruct.py

License

Maintained by Ante MARIĆ and licensed under the GPL-3.0 License.

Copyright (c) 2024 Idiap Research Institute contact@idiap.ch