Google Summer of Code
Open3D team
About the Organization
A Modern Library for 3D Data Processing
Primary Technologies & Languages
pythonc++cudatensorflowpytorch
Technical Topics & Focus Areas
3D machine learning3D data processing3D visualizationphysically based rendering
Participation Years
Completed Projects Distribution
Annual Completed Projects History
Open3D teamHover over bars to view completed counts
4
3
2
1
0
2022
Projects3
Years Active1
Stackpython
TierTier 1
Past Projects
Showing 3 of 3 projects. Click any project card for scope, mentors, and proposal studio.
Open3D visualization on cloud notebooks
intermediateMentors: Student: fanxiaolin
Open3D is an open-source library that supports the rapid development of software that deals with 3D data. The Open3D frontend exposes a set of carefully selected data structures and algorithms in both C++ and Python. Open3D visualization does not work consistently on cloud notebooks such as Google Colab. This is due to a need to modernize the Open3D Jupyter widget as well as use CPU rendering when a GPU with graphics capability is not available in the cloud notebook. This will enable new users to start working with Open3D visualization quickly by directly opening tutorial notebooks in the cloud with no GPU devices. This proposal is based on the original GSoC idea in https://github.com/isl-org/Open3D/wiki/Google-Summer-of-Code-2022#3-open3d-visualization-on-cloud-notebooks.
Geometry processing algorithms for tensor-based geometry
intermediateMentors: Student: Zhengyu
The proposal aims to enhance the functionality of Open3D tensor-based geometry processing algorithms. First of all, functions related to surface reconstruction and geometry sampling will be wrapped to support the tensor-based module. Secondly, I will enhance the functionality of tensor-based geometry file IO capabilities, including geometry file IO module and geometry creation APIs. Besides, I intend to wrap other classes and functions to make the tensor-based APIs reach feature parity with the old APIs if time permits.
Open3D-ML 3D Object detection model (PV-RCNN++)
advancedMentors: Student: Narayanan E.R.
The objective of the project is to implement a new state-of-the-art 3D Object detection model (PV-RCNN++) in PyTorch and TensorFlow and train it on large-scale datasets such as Waymo to reproduce the original author’s results. The expected outcomes of the project are: • Implement PV-RCNN++ training and evaluation with PyTorch. • Train and reproduce results on the Waymo dataset. • Port the PyTorch implementation to TensorFlow and ensure results can be reproduced. • Proper documentation about model usage and config customizations with tutorials and examples. The implementation plan is as follows: • Using OpenPCDet implementation as a reference, implement PV-RCNN++ in PyTorch and benchmark on Waymo Open Dataset. • Using this implemented PyTorch model as a reference, implement each corresponding layer in Tensorflow and test it. Convert the PyTorch model to TensorFlow and verify the metrics on the Waymo dataset. • Once verified, train the model from scratch in TensorFlow and verify the results.