2 citations · 4 across the 6 of their papers we have counts for
6 papers
AutoOcc: Automatic Open-Ended Semantic Occupancy Annotation via Vision-Language Guided Gaussian Splatting
Xiaoyu Zhou, Jingqi Wang, Yongtao Wang +3
Obtaining high-quality 3D semantic occupancy from raw sensor data remains an essential yet challenging task, often requiring extensive manual labeling. In this work, we propose Aut…
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…
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…
A Simple and Generic Framework for Feature Distillation via Channel-wise Transformation
Ziwei Liu, Yongtao Wang, Xiaojie Chu
Knowledge distillation is a popular technique for transferring the knowledge from a large teacher model to a smaller student model by mimicking. However, distillation by directly a…
BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving Scenarios
Zhiwei Lin, Yongtao Wang, Shengxiang Qi +2
Existing LiDAR-based 3D object detection methods for autonomous driving scenarios mainly adopt the training-from-scratch paradigm. Unfortunately, this paradigm heavily relies on la…