mlsanityData to 7 Oct 2026

ALOOD: Exploiting Language Representations for LiDAR-based Out-of-Distribution Object Detection

arXiv 2603.08180 ↗First posted 9 Mar 2026cs.CV cs.LGcited by 0

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Abstract (arXiv)

LiDAR-based 3D object detection plays a critical role for reliable and safe autonomous driving systems. However, existing detectors often produce overly confident predictions for objects not belonging to known categories, posing significant safety risks. This is caused by so-called out-of-distribution (OOD) objects, which were not part of the training data, resulting in incorrect predictions. To address this challenge, we propose ALOOD (Aligned LiDAR representations for Out-Of-Distribution Detection), a novel approach that incorporates language representations from a vision-language model (VLM). By aligning the object features from the object detector to the feature space of the VLM, we can treat the detection of OOD objects as a zero-shot classification task. We demonstrate competitive performance on the nuScenes OOD benchmark, establishing a novel approach to OOD object detection in LiDAR using language representations. The source code is available at https://github.com/uulm-mrm/mmood3d.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 34 of its 43 references that are in the map. First posted on arXiv 9 Mar 2026.

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34 of 43 references.

References · 34

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22 more
  1. 2021Open-vocabulary Object Detection via Vision and Language Knowledge Distillationcited by 903
  2. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  3. 2019Scaling Out-of-Distribution Detection for Real-World Settingscited by 465
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  12. 2021Neural Mean Discrepancy for Efficient Out-of-Distribution Detectioncited by 42
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  15. 2022LidarCLIP or: How I Learned to Talk to Point Cloudscited by 33
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  1. 2024Find n' Propagate: Open-Vocabulary 3D Object Detection in Urban Environments
  2. 2024Global-Local Collaborative Inference with LLM for Lidar-Based Open-Vocabulary Detection
  3. 2025Leveraging Text-Driven Semantic Variation for Robust OOD Segmentation
  4. 2024Open-Vocabulary Object Detectors: Robustness Challenges under Distribution Shifts
  5. 2025Exploiting Vision Language Model for Training-Free 3D Point Cloud OOD Detection via Graph Score Propagation
  6. 2025An Empirical Analysis of VLM-based OOD Detection: Mechanisms, Advantages, and Sensitivity
  7. 2021LiDAR Cluster First and Camera Inference Later: A New Perspective Towards Autonomous Driving
  8. 2025Harnessing Large Language and Vision-Language Models for Robust Out-of-Distribution Detection