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most citedIs Your HD Map Constructor Reliable under Sensor Corruptions?

2 citations · 3 across the 7 of their papers we have counts for

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cs.CV2025

Synergistic Prompting for Robust Visual Recognition with Missing Modalities

Zhihui Zhang, Luanyuan Dai, Qika Lin +5

Large-scale multi-modal models have demonstrated remarkable performance across various visual recognition tasks by leveraging extensive paired multi-modal training data. However, i…

cs.CV2025

SafeMap: Robust HD Map Construction from Incomplete Observations

Xiaoshuai Hao, Lingdong Kong, Rong Yin +4

Robust high-definition (HD) map construction is vital for autonomous driving, yet existing methods often struggle with incomplete multi-view camera data. This paper presents SafeMa…

cs.CV2024★ 1 cited

MapDistill: Boosting Efficient Camera-based HD Map Construction via Camera-LiDAR Fusion Model Distillation

Xiaoshuai Hao, Ruikai Li, Hui Zhang +7

Online high-definition (HD) map construction is an important and challenging task in autonomous driving. Recently, there has been a growing interest in cost-effective multi-view ca…

cs.CV2024

The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition

Lingdong Kong, Shaoyuan Xie, Hanjiang Hu +88

In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sen…

cs.CV2024

Team Samsung-RAL: Technical Report for 2024 RoboDrive Challenge-Robust Map Segmentation Track

Xiaoshuai Hao, Yifan Yang, Hui Zhang +4

In this report, we describe the technical details of our submission to the 2024 RoboDrive Challenge Robust Map Segmentation Track. The Robust Map Segmentation track focuses on the…