An Explicit Frame Construction for Normalizing 3D Point Clouds

Justin Baker, Shih-Hsin Wang, Tommaso De Fernex, Bao Wang
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:2456-2473, 2024.

Abstract

Many real-world datasets are represented as 3D point clouds – yet they often lack a predefined reference frame, posing a challenge for machine learning or general data analysis. Traditional methods for determining reference frames and normalizing 3D point clouds often struggle with specific inputs, lack theoretical guarantees, or require massive data. We introduce a new algorithm that overcomes these limitations and guarantees both universality and compatibility with any learnable framework for 3D point cloud analysis. Our algorithm works with any input point cloud and performs consistently regardless of input complexities, unlike data-driven methods that are susceptible to biases or limited training data. Empirically, our algorithm outperforms existing methods in effectiveness and generalizability across diverse benchmark datasets. Code is available at https://github.com/Utah-Math-Data-Science/alignment.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-baker24a, title = {An Explicit Frame Construction for Normalizing 3{D} Point Clouds}, author = {Baker, Justin and Wang, Shih-Hsin and De Fernex, Tommaso and Wang, Bao}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {2456--2473}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/baker24a/baker24a.pdf}, url = {https://proceedings.mlr.press/v235/baker24a.html}, abstract = {Many real-world datasets are represented as 3D point clouds – yet they often lack a predefined reference frame, posing a challenge for machine learning or general data analysis. Traditional methods for determining reference frames and normalizing 3D point clouds often struggle with specific inputs, lack theoretical guarantees, or require massive data. We introduce a new algorithm that overcomes these limitations and guarantees both universality and compatibility with any learnable framework for 3D point cloud analysis. Our algorithm works with any input point cloud and performs consistently regardless of input complexities, unlike data-driven methods that are susceptible to biases or limited training data. Empirically, our algorithm outperforms existing methods in effectiveness and generalizability across diverse benchmark datasets. Code is available at https://github.com/Utah-Math-Data-Science/alignment.} }
Endnote
%0 Conference Paper %T An Explicit Frame Construction for Normalizing 3D Point Clouds %A Justin Baker %A Shih-Hsin Wang %A Tommaso De Fernex %A Bao Wang %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-baker24a %I PMLR %P 2456--2473 %U https://proceedings.mlr.press/v235/baker24a.html %V 235 %X Many real-world datasets are represented as 3D point clouds – yet they often lack a predefined reference frame, posing a challenge for machine learning or general data analysis. Traditional methods for determining reference frames and normalizing 3D point clouds often struggle with specific inputs, lack theoretical guarantees, or require massive data. We introduce a new algorithm that overcomes these limitations and guarantees both universality and compatibility with any learnable framework for 3D point cloud analysis. Our algorithm works with any input point cloud and performs consistently regardless of input complexities, unlike data-driven methods that are susceptible to biases or limited training data. Empirically, our algorithm outperforms existing methods in effectiveness and generalizability across diverse benchmark datasets. Code is available at https://github.com/Utah-Math-Data-Science/alignment.
APA
Baker, J., Wang, S., De Fernex, T. & Wang, B.. (2024). An Explicit Frame Construction for Normalizing 3D Point Clouds. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:2456-2473 Available from https://proceedings.mlr.press/v235/baker24a.html.

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