mlsanityData to 14 Oct 2026

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

arXiv 2508.01269 ↗First posted 2 Aug 2025cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 42 references that are in the map. First posted on arXiv 2 Aug 2025.

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27 of 42 references.

References · 27

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  3. 2017On Calibration of Modern Neural Networkscited by 4,960
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  5. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  6. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  7. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  8. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  9. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  10. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  11. 2020Point Transformercited by 1,470
  12. 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
15 more
  1. 2019Measuring Calibration in Deep Learningcited by 446
  2. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 388
  3. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 379
  4. 20196-DOF GraspNet: Variational Grasp Generation for Object Manipulationcited by 242
  5. 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
  6. 2021Walk in the Cloud: Learning Curves for Point Clouds Shape Analysiscited by 172
  7. 2017Semantic3D.net: A new Large-scale Point Cloud Classification Benchmarkcited by 158
  8. 2018Generating 3D Adversarial Point Cloudscited by 144
  9. 2018PointCloud Saliency Mapscited by 112
  10. 2018DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensorcited by 95
  11. 2019Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifierscited by 92
  12. 2022Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptionscited by 80
  13. 2023Point2Vec for Self-Supervised Representation Learning on Point Cloudscited by 28
  14. 2022Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Cloudscited by 8
  15. 2025DG-MVP: 3D Domain Generalization via Multiple Views of Point Clouds for Classificationcited by 1

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