ModelNet40-E: An Uncertainty-Aware Benchmark for Point Cloud Classification
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911180900007936 |
|---|---|
| author | Alonso, Pedro Li, Tianrui Li, Chongshou |
| author_facet | Alonso, Pedro Li, Tianrui Li, Chongshou |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01269 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ModelNet40-E: An Uncertainty-Aware Benchmark for Point Cloud Classification Alonso, Pedro Li, Tianrui Li, Chongshou Computer Vision and Pattern Recognition 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. |
| title | ModelNet40-E: An Uncertainty-Aware Benchmark for Point Cloud Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.01269 |