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arXiv · 2508.01269

ModelNet40-E: An Uncertainty-Aware Benchmark for Point Cloud Classification

Pedro Alonso·Tianrui Li·Chongshou Li
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Abstract

We introduce ModelNet40-E, a new benchmark designed to assess the robustness and calibration of point cloud classification models under synthetic LiDAR-like noise. Unlike existing benchmarks, ModelNet40-E provides both noise-corrupted point clouds and point-wise uncertainty annotations via Gaussian noise parameters ({\sigma}, {\mu}), enabling fine-grained evaluation of uncertainty modeling. We evaluate three popular models-PointNet, DGCNN, and Point Transformer v3-across multiple noise levels using classification accuracy, calibration metrics, and uncertainty-awareness. While all models degrade under increasing noise, Point Transformer v3 demonstrates superior calibration, with predicted uncertainties more closely aligned with the underlying measurement uncertainty.

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Pedro Alonso, Tianrui Li, Chongshou Li. 2025-08-02. ModelNet40-E: An Uncertainty-Aware Benchmark for Point Cloud Classification. https://arxiv.org/abs/2508.01269

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