202 citations · 232 across the 25 of their papers we have counts for
5 papers · 1 filter
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…
Foreground Guidance and Multi-Layer Feature Fusion for Unsupervised Object Discovery with Transformers
Zhiwei Lin, Zengyu Yang, Yongtao Wang
Unsupervised object discovery (UOD) has recently shown encouraging progress with the adoption of pre-trained Transformer features. However, current methods based on Transformers ma…
FlowNAS: Neural Architecture Search for Optical Flow Estimation
Zhiwei Lin, Tingting Liang, Taihong Xiao +3
Existing optical flow estimators usually employ the network architectures typically designed for image classification as the encoder to extract per-pixel features. However, due to…
Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection
Kaicheng Yu, Tang Tao, Hongwei Xie +10
There are two critical sensors for 3D perception in autonomous driving, the camera and the LiDAR. The camera provides rich semantic information such as color, texture, and the LiDA…
BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework
Tingting Liang, Hongwei Xie, Kaicheng Yu +6
Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to lever…