Papers › TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis

TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis

26 Nov 2022CVPR 2024 1arXiv:2211.14456archive 2025-07-28

Pavlo Melnyk, Andreas Robinson, Michael Felsberg, Mårten Wadenbäck

In many practical applications, 3D point cloud analysis requires rotation invariance. In this paper, we present a learnable descriptor invariant under 3D rotations and reflections, i.e., the O(3) actions, utilizing the recently introduced steerable 3D spherical neurons and vector neurons. Specifically, we propose an embedding of the 3D spherical neurons into 4D vector neurons, which leverages end-to-end training of the model. In our approach, we perform TetraTransform--an equivariant embedding of the 3D input into 4D, constructed from the steerable neurons--and extract deeper O(3)-equivariant features using vector neurons. This integration of the TetraTransform into the VN-DGCNN framework, termed TetraSphere, negligibly increases the number of parameters by less than 0.0002%. TetraSphere sets a new state-of-the-art performance classifying randomly rotated real-world object scans of the challenging subsets of ScanObjectNN. Additionally, TetraSphere outperforms all equivariant methods on randomly rotated synthetic data: classifying objects from ModelNet40 and segmenting parts of the ShapeNet shapes. Thus, our results reveal the practical value of steerable 3D spherical neurons for learning in 3D Euclidean space. The code is available at https://github.com/pavlo-melnyk/tetrasphere.

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build_datasets pavlo-melnyk/tetrasphere/tetrasphere/runner.py official repository ran MIT (permissive) · 6550b40e98377f7d · report
cal_loss pavlo-melnyk/tetrasphere/tetrasphere/utils.py official repository ran · fixture could not drive it MIT (permissive) · c6c2758c0c2fa756 · report
calculate_shape_IoU pavlo-melnyk/tetrasphere/tetrasphere/utils.py official repository ran MIT (permissive) · ed28493d8df0a024 · report
embed pavlo-melnyk/tetrasphere/tetrasphere/models/spheres.py official repository ran MIT (permissive) · e4b543f085e11201 · report
embed_spheres pavlo-melnyk/tetrasphere/tetrasphere/models/spheres.py official repository ran MIT (permissive) · c8619d0a79c0c42d · report
get_graph_feature pavlo-melnyk/tetrasphere/tetrasphere/models/utils.py official repository ran MIT (permissive) · e146f86f54b1a708 · report
knn pavlo-melnyk/tetrasphere/tetrasphere/models/utils.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · cdd0141594039dcb · report
mean_pool pavlo-melnyk/tetrasphere/tetrasphere/models/vnn.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 19b639ea820e6dbc · report
nd_get_graph_feature pavlo-melnyk/tetrasphere/tetrasphere/models/utils.py official repository ran MIT (permissive) · 111defecda8c385d · report
append_ones pavlo-melnyk/tetrasphere/tetrasphere/models/spheres.py official repository unverified MIT (permissive) · 3a71074992fa1e26 · report

Tasks

3D Point Cloud ClassificationPoint Cloud Classification

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Methods

DGCNN

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