mlsanityData to 7 Oct 2026

Out-of-Distribution Detection for LiDAR-based 3D Object Detection

arXiv 2209.14435 ↗First posted 28 Sep 2022cs.CV cs.AI cs.LGcited by 17

Not on the map. Why?

Abstract (arXiv)

3D object detection is an essential part of automated driving, and deep neural networks (DNNs) have achieved state-of-the-art performance for this task. However, deep models are notorious for assigning high confidence scores to out-of-distribution (OOD) inputs, that is, inputs that are not drawn from the training distribution. Detecting OOD inputs is challenging and essential for the safe deployment of models. OOD detection has been studied extensively for the classification task, but it has not received enough attention for the object detection task, specifically LiDAR-based 3D object detection. In this paper, we focus on the detection of OOD inputs for LiDAR-based 3D object detection. We formulate what OOD inputs mean for object detection and propose to adapt several OOD detection methods for object detection. We accomplish this by our proposed feature extraction method. To evaluate OOD detection methods, we develop a simple but effective technique of generating OOD objects for a given object detection model. Our evaluation based on the KITTI dataset shows that different OOD detection methods have biases toward detecting specific OOD objects. It emphasizes the importance of combined OOD detection methods and more research in this direction.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 38 of its 49 references that are in the map. First posted on arXiv 28 Sep 2022.

Builds on

38 of 49 references.

References · 38

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26 more
  1. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  2. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  3. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
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  5. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
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  7. 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 511
  8. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  9. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  10. 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
  11. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  12. 2018Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifierscited by 126
  13. 2021SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloudcited by 119
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  20. 2021Uncertainty for Identifying Open-Set Errors in Visual Object Detectioncited by 26
  21. 2019Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Outputcited by 20
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  23. 2019Efficacy of Pixel-Level OOD Detection for Semantic Segmentationcited by 9
  24. 2021Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learningcited by 9
  25. 2021Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODscited by 9
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 6; 2026: 4.

17 papers.

Sort:

  1. 2024Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detectioncited by 11
  2. 2025Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Drivingcited by 10
  3. 2023LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesiscited by 7
  4. 2024Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representationscited by 6
  5. 2024Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation Patternscited by 3
  6. 2024HD-OOD3D: Supervised and Unsupervised Out-of-Distribution object detection in LiDAR datacited by 2
  7. 2025Out-of-Distribution Detection in LiDAR Semantic Segmentation Using Epistemic Uncertainty from Hierarchical GMMscited by 2
  8. 2025Towards 3D Objectness Learning in an Open Worldcited by 2
  9. 2025Relative Energy Learning for LiDAR Out-of-Distribution Detectioncited by 2
  10. 2023Out-of-distribution Object Detection through Bayesian Uncertainty Estimationcited by 1
  11. 2024UOD: Unseen Object Detection in 3D Point Cloudcited by 0
  12. 2025SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagationcited by 0
5 more
  1. 2025Detecting What Matters: A Novel Approach for Out-of-Distribution 3D Object Detection in Autonomous Vehiclescited by 0
  2. 2026RAD: Retrieval-Augmented Monocular Metric Depth Estimation for Underrepresented Classescited by 0
  3. 2026ALOOD: Exploiting Language Representations for LiDAR-based Out-of-Distribution Object Detectioncited by 0
  4. 2026ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Predictioncited by 0
  5. 2026Neural Distribution Prior for LiDAR Out-of-Distribution Detectioncited by 0