Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook

[PDF] arxiv.org

Graphbev: Towards robust bev feature alignment for multi-modal 3d object detection

Z Song, L Yang, S Xu, L Liu, D Xu, C Jia, F Jia… - arXiv preprint arXiv …, 2024 - arxiv.org
Integrating LiDAR and camera information into Bird's-Eye-View (BEV) representation has
emerged as a crucial aspect of 3D object detection in autonomous driving. However …
被引用次数：4 相关文章 所有 2 个版本
[PDF] arxiv.org

Calib3d: Calibrating model preferences for reliable 3d scene understanding

L Kong, X Xu, J Cen, W Zhang, L Pan, K Chen… - arXiv preprint arXiv …, 2024 - arxiv.org
Safety-critical 3D scene understanding tasks necessitate not only accurate but also
confident predictions from 3D perception models. This study introduces Calib3D, a …
被引用次数：3 相关文章 所有 3 个版本
[PDF] arxiv.org

Fuzzy-nms: Improving 3d object detection with fuzzy classification in nms

L Wang, X Zhang, F Zhao, C Wu… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Non-maximum suppression (NMS) is an essential post-processing module in many 3D
object detection frameworks to remove overlapping candidate bounding boxes. However, an …
被引用次数：1 相关文章 所有 2 个版本
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Optimizing LiDAR Placements for Robust Driving Perception in Adverse Conditions

Y Li, L Kong, H Hu, X Xu, X Huang - arXiv preprint arXiv:2403.17009, 2024 - arxiv.org
The robustness of driving perception systems under unprecedented conditions is crucial for
safety-critical usages. Latest advancements have prompted increasing interests towards …
被引用次数：1 相关文章 所有 3 个版本
[PDF] arxiv.org

ContrastAlign: Toward Robust BEV Feature Alignment via Contrastive Learning for Multi-Modal 3D Object Detection

Z Song, F Jia, H Pan, Y Luo, C Jia, G Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
In the field of 3D object detection tasks, fusing heterogeneous features from LiDAR and
camera sensors into a unified Bird's Eye View (BEV) representation is a widely adopted …
相关文章 所有 2 个版本
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Towards Scenario Generalization for Vision-based Roadside 3D Object Detection

L Yang, X Zhang, J Li, L Wang, C Zhang, L Ju… - arXiv preprint arXiv …, 2024 - arxiv.org
Roadside perception can greatly increase the safety of autonomous vehicles by extending
their perception ability beyond the visual range and addressing blind spots. However …
被引用次数：1 相关文章 所有 2 个版本
[PDF] arxiv.org

VTruST: Controllable value function based subset selection for Data-Centric Trustworthy AI

S Das, S Nag, S Sharma, S Bhattacharya… - arXiv preprint arXiv …, 2024 - arxiv.org
Trustworthy AI is crucial to the widespread adoption of AI in high-stakes applications with
fairness, robustness, and accuracy being some of the key trustworthiness metrics. In this …
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