168 citations · 281 across the 9 of their papers we have counts for
8 papers · 1 filter
Efficient and Deterministic Search Strategy Based on Residual Projections for Point Cloud Registration with Correspondences
Xinyi Li, Hu Cao, Yinlong Liu +3
Estimating the rigid transformation between two LiDAR scans through putative 3D correspondences is a typical point cloud registration paradigm. Current 3D feature matching approach…
NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields
Junge Zhang, Feihu Zhang, Shaochen Kuang +1
Labeling LiDAR point clouds for training autonomous driving is extremely expensive and difficult. LiDAR simulation aims at generating realistic LiDAR data with labels for training…
Single-view Neural Radiance Fields with Depth Teacher
Yurui Chen, Chun Gu, Feihu Zhang +1
Neural Radiance Fields (NeRF) have been proposed for photorealistic novel view rendering. However, it requires many different views of one scene for training. Moreover, it has poor…
S-NeRF: Neural Radiance Fields for Street Views
Ziyang Xie, Junge Zhang, Wenye Li +2
Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects and scenes, given the object-centric camera views with large overlaps. However, we conjugate that this parad…
Unsupervised Contrastive Domain Adaptation for Semantic Segmentation
Feihu Zhang, Vladlen Koltun, Philip Torr +2
Semantic segmentation models struggle to generalize in the presence of domain shift. In this paper, we introduce contrastive learning for feature alignment in cross-domain adaptati…
Domain-invariant Stereo Matching Networks
Feihu Zhang, Xiaojuan Qi, Ruigang Yang +3
State-of-the-art stereo matching networks have difficulties in generalizing to new unseen environments due to significant domain differences, such as color, illumination, contrast,…
GA-Net: Guided Aggregation Net for End-to-end Stereo Matching
Feihu Zhang, Victor Prisacariu, Ruigang Yang +1
In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities. We propose…
Augmented LiDAR Simulator for Autonomous Driving
Jin Fang, Dingfu Zhou, Feilong Yan +5
In Autonomous Driving (AD), detection and tracking of obstacles on the roads is a critical task. Deep-learning based methods using annotated LiDAR data have been the most widely ad…