Comprehensive Robustness Analysis of LiDAR-based 3D Object Detection in Autonomous Driving

• URL: http://arxiv.org/abs/2607.02074v1
• Date: Thu, 02 Jul 2026 12:13:49 GMT
• Title: Comprehensive Robustness Analysis of LiDAR-based 3D Object Detection in Autonomous Driving
• Abstract summary: Recent advancements in LiDAR-only 3D object detection have demonstrated improved detection accuracy over benchmark datasets.<n>Very few adversarial robustness studies exist for LiDAR-only 3D object detection.<n>We propose a holistic framework that evaluates adversarial robustness using two structural factors and three predictive factors.
• Score: 10.67458307610481
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in LiDAR-only 3D object detection have demonstrated improved detection accuracy over benchmark datasets. However, the adversarial robustness of these models remains untested. Very few adversarial robustness studies exist for LiDAR-only 3D object detection and unfortunately, even they are limited to legacy models. Moreover, there is a systemic gap in the existing evaluation frameworks that rely simply on mAP ignoring other structural and predictive factors. To fill this gap, we propose a holistic framework that evaluates adversarial robustness using two structural factors (point cloud density and point cloud localization) and three predictive factors (misclassification, localization error, distance from ego). Using this framework, we perform an empirical study and critical analysis on recent and legacy state-of-the-art models using adversarial attacks specifically designed for LiDAR-based models. Our key finding is that high-capacity, voxel-based detectors are more susceptible to structured coordinate perturbations than pillar-based detectors. Additionally, non-anchor-based detectors demonstrate poor adversarial robustness, which necessitates rethinking model training techniques. Overall, our results demonstrate that recent models are as vulnerable to adversarial attacks as their predecessors. Therefore, we argue that there is a need to improve the evaluation benchmarks for 3D object detection that not only reward architectural modifications for improving detection accuracy, but also evaluate whether the design choices improve adversarial robustness.

Related papers

• Explainability-Aware Frustum Attack: Exposing Structural Vulnerabilities in LiDAR-Based 3D Object Detectors [5.47472857635307]
  Saliency-LiDAR method produces universal saliency maps for LiDAR-based 3D object detectors.<n>We design the Explainability-aware Frustum Attack (EFA), which selectively perturbs only the most influential frustums rather than uniformly attacking entire object regions.<n>EFA reduces detection recall by more than 15 percentage points while requiring 25-50% fewer frustums than the state-of-the-art non-saliency-aware baseline.
  arXiv  Detail & Related papers  (2026-06-29T08:40:16Z)
• Rethinking Evaluation of Infrared Small Target Detection [105.59753496831739]
  This paper introduces a hybrid-level metric incorporating pixel- and target-level performance, proposing a systematic error analysis method, and emphasizing the importance of cross-dataset evaluation.<n>An open-source toolkit has be released to facilitate standardized benchmarking.
  arXiv  Detail & Related papers  (2025-09-21T02:45:07Z)
• It Only Gets Worse: Revisiting DL-Based Vulnerability Detectors from a Practical Perspective [14.271145160443462]
  VulTegra compares scratch-trained and pre-trained DL models for vulnerability detection.<n>State-of-the-art (SOTA) detectors still suffer from low consistency, limited real-world capabilities, and scalability challenges.
  arXiv  Detail & Related papers  (2025-07-13T08:02:56Z)
• RoHOI: Robustness Benchmark for Human-Object Interaction Detection [84.78366452133514]
  Human-Object Interaction (HOI) detection is crucial for robot-human assistance, enabling context-aware support.<n>We introduce the first benchmark for HOI detection, evaluating model resilience under diverse challenges.<n>Our benchmark, RoHOI, includes 20 corruption types based on the HICO-DET and V-COCO datasets and a new robustness-focused metric.
  arXiv  Detail & Related papers  (2025-07-12T01:58:04Z)
• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Open-Vocabulary Object Detectors: Robustness Challenges under Distribution Shifts [6.486569431242123]
  Vision-Language Models (VLMs) have recently achieved groundbreaking results. Investigating OOD robustness in VLM object detection is essential to increase the trustworthiness of these models. This study presents a comprehensive robustness evaluation of the zero-shot capabilities of three recent open-vocabulary (OV) foundation object detection models.
  arXiv  Detail & Related papers  (2024-04-01T14:18:15Z)
• AdvMono3D: Advanced Monocular 3D Object Detection with Depth-Aware Robust Adversarial Training [64.14759275211115]
  We propose a depth-aware robust adversarial training method for monocular 3D object detection, dubbed DART3D. Our adversarial training approach capitalizes on the inherent uncertainty, enabling the model to significantly improve its robustness against adversarial attacks.
  arXiv  Detail & Related papers  (2023-09-03T07:05:32Z)
• On the Adversarial Robustness of Camera-based 3D Object Detection [21.091078268929667]
  We investigate the robustness of leading camera-based 3D object detection approaches under various adversarial conditions. We find that bird's-eye-view-based representations exhibit stronger robustness against localization attacks. depth-estimation-free approaches have the potential to show stronger robustness. incorporating multi-frame benign inputs can effectively mitigate adversarial attacks.
  arXiv  Detail & Related papers  (2023-01-25T18:59:15Z)
• Benchmarking the Robustness of LiDAR Semantic Segmentation Models [78.6597530416523]
  In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. We propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. We design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications.
  arXiv  Detail & Related papers  (2023-01-03T06:47:31Z)
• A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks [84.10546708708554]
  3D object detectors are increasingly crucial for security-critical tasks. It is imperative to understand their robustness against adversarial attacks. This paper presents the first comprehensive evaluation and analysis of the robustness of LiDAR-based 3D detectors under adversarial attacks.
  arXiv  Detail & Related papers  (2022-12-20T13:09:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.