新基准评估点云模型在模拟激光雷达噪声下的鲁棒性与不确定性建模能力
ModelNet40-E: An Uncertainty-Aware Benchmark for Point Cloud Classification
- 引入带点级不确定度标注的合成噪声点云数据集
- 点云模型在噪声下准确率下降，但点变换器表现更优
- 适合研究点云鲁棒性与不确定性建模的开发者
我们提出ModelNet40-E，一个用于评估点云分类模型在合成激光雷达类噪声下鲁棒性与校准能力的新基准。不同于现有基准，ModelNet40-E提供噪声污染的点云数据及基于高斯噪声参数（σ, μ）的点级不确定性标注，支持细粒度的不确定性建模评估。我们在多个噪声水平下评估了PointNet、DGCNN和Point Transformer v3三种主流模型，使用分类准确率、校准指标和不确定性感知性能进行分析。尽管所有模型在噪声增加时性能均下降，但Point Transformer v3展现出更优的校准能力，其预测不确定性与底层测量不确定性更为一致。
原文摘要 · Abstract (English)
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 (σ, μ), 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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