J Sun, X Xu, L Kong, Y Liu, L Li, C Zhu, J Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is crucial for understanding complex 3D environments. Traditional approaches often rely on …
Recent advancements in bird's eye view (BEV) representations have shown remarkable promise for in-vehicle 3D perception. However, while these methods have achieved …
In the realm of autonomous driving, accurate 3D perception is the foundation. However, developing such models relies on extensive human annotations--a process that is both …
Driving systems often rely on high-definition (HD) maps for precise environmental information, which is crucial for planning and navigation. While current HD map constructors …
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 …
D Fu, Y Lin, H Yang, H Li, Y Luo, X Cheng, Y Xu - researchgate.net
The failure of vehicle-mounted cameras and LiDAR sensors is inevitable in practice, resulting in incomplete data. To enhance the ability to robustly handle incomplete data, we …
This paper describes the methodology and results of Track 5-Robust Multi-Modal BEV Detection in the 2024 Robo-Drive Challenge. This track focuses on 3D scene perception …
The advent of bird's-eye view (BEV) representation has witnessed significant advancements in camera-only 3D object detection. However, existing approaches usually struggle when …
In this paper, we elaborate on the practical application and demonstration of Cross-Modal Transformer (CMT) for Track 5–Robust Multi-Modal BEV Detection, in the 2024 RoboDrive …