5 citations · 14 across the 20 of their papers we have counts for
7 papers · 1 filter
OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection
Zhongyu Xia, Jishuo Li, Zhiwei Lin +3
Open-world perception aims to develop a model adaptable to novel domains and various sensor configurations and can understand uncommon objects and corner cases. However, current re…
TEOcc: Radar-camera Multi-modal Occupancy Prediction via Temporal Enhancement
Zhiwei Lin, Hongbo Jin, Yongtao Wang +2
As a novel 3D scene representation, semantic occupancy has gained much attention in autonomous driving. However, existing occupancy prediction methods mainly focus on designing bet…
Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts
Zhiwei Lin, Yongtao Wang, Zhi Tang
Existing perception models achieve great success by learning from large amounts of labeled data, but they still struggle with open-world scenarios. To alleviate this issue, researc…
RCBEVDet++: Toward High-accuracy Radar-Camera Fusion 3D Perception Network
Zhiwei Lin, Zhe Liu, Yongtao Wang +2
Perceiving the surrounding environment is a fundamental task in autonomous driving. To obtain highly accurate perception results, modern autonomous driving systems typically employ…
HENet: Hybrid Encoding for End-to-end Multi-task 3D Perception from Multi-view Cameras
Zhongyu Xia, ZhiWei Lin, Xinhao Wang +5
Three-dimensional perception from multi-view cameras is a crucial component in autonomous driving systems, which involves multiple tasks like 3D object detection and bird's-eye-vie…
RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection
Zhiwei Lin, Zhe Liu, Zhongyu Xia +7
Three-dimensional object detection is one of the key tasks in autonomous driving. To reduce costs in practice, low-cost multi-view cameras for 3D object detection are proposed to r…