12 citations · 32 across the 12 of their papers we have counts for
19 papers
Make Your ViT-based Multi-view 3D Detectors Faster via Token Compression
Dingyuan Zhang, Dingkang Liang, Zichang Tan +4
Slow inference speed is one of the most crucial concerns for deploying multi-view 3D detectors to tasks with high real-time requirements like autonomous driving. Although many spar…
LION: Linear Group RNN for 3D Object Detection in Point Clouds
Zhe Liu, Jinghua Hou, Xinyu Wang +4
The benefit of transformers in large-scale 3D point cloud perception tasks, such as 3D object detection, is limited by their quadratic computation cost when modeling long-range rel…
Explore the LiDAR-Camera Dynamic Adjustment Fusion for 3D Object Detection
Yiran Yang, Xu Gao, Tong Wang +5
Camera and LiDAR serve as informative sensors for accurate and robust autonomous driving systems. However, these sensors often exhibit heterogeneous natures, resulting in distribut…
Exploring the Causality of End-to-End Autonomous Driving
Jiankun Li, Hao Li, Jiangjiang Liu +6
Deep learning-based models are widely deployed in autonomous driving areas, especially the increasingly noticed end-to-end solutions. However, the black-box property of these model…
OPEN: Object-wise Position Embedding for Multi-view 3D Object Detection
Jinghua Hou, Tong Wang, Xiaoqing Ye +6
Accurate depth information is crucial for enhancing the performance of multi-view 3D object detection. Despite the success of some existing multi-view 3D detectors utilizing pixel-…
SEED: A Simple and Effective 3D DETR in Point Clouds
Zhe Liu, Jinghua Hou, Xiaoqing Ye +3
Recently, detection transformers (DETRs) have gradually taken a dominant position in 2D detection thanks to their elegant framework. However, DETR-based detectors for 3D point clou…