J Mao, S Shi, X Wang, H Li - International Journal of Computer Vision, 2023 - Springer
Autonomous driving, in recent years, has been receiving increasing attention for its potential to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …
Abstract 3D object detectors usually rely on hand-crafted proxies, eg, anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be …
Lidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. While prevalent multi-modal methods simply decorate raw …
R Xu, X Xia, J Li, H Li, S Zhang, Z Tu… - Proceedings of the …, 2023 - openaccess.thecvf.com
Modern perception systems of autonomous vehicles are known to be sensitive to occlusions and lack the capability of long perceiving range. It has been one of the key bottlenecks that …
X Lai, Y Chen, F Lu, J Liu, J Jia - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
LiDAR-based 3D point cloud recognition has benefited various applications. Without specially considering the LiDAR point distribution, most current methods suffer from …
This paper is not motivated to seek innovation within the attention mechanism. Instead it focuses on overcoming the existing trade-offs between accuracy and efficiency within the …
Transformer, an attention-based encoder–decoder model, has already revolutionized the field of natural language processing (NLP). Inspired by such significant achievements, some …
Abstract 3D object detection in point clouds is a core component for modern robotics and autonomous driving systems. A key challenge in 3D object detection comes from the …
LiDAR-camera fusion methods have shown impressive performance in 3D object detection. Recent advanced multi-modal methods mainly perform global fusion, where image features …