34 citations · 57 across the 9 of their papers we have counts for
10 papers
Language-Guided 3D Object Detection in Point Cloud for Autonomous Driving
Wenhao Cheng, Junbo Yin, Wei Li +2
This paper addresses the problem of 3D referring expression comprehension (REC) in autonomous driving scenario, which aims to ground a natural language to the targeted region in Li…
SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud
Yan Wang, Junbo Yin, Wei Li +3
LiDAR-based 3D object detection is an indispensable task in advanced autonomous driving systems. Though impressive detection results have been achieved by superior 3D detectors, th…
Zero-shot point cloud segmentation by transferring geometric primitives
Runnan Chen, Xinge Zhu, Nenglun Chen +4
We investigate transductive zero-shot point cloud semantic segmentation, where the network is trained on seen objects and able to segment unseen objects. The 3D geometric elements…
An Intelligent Self-driving Truck System For Highway Transportation
Dawei Wang, Lingping Gao, Ziquan Lan +9
Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing works mainly focus on cars, extra…
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception
Xinge Zhu, Hui Zhou, Tai Wang +6
State-of-the-art methods for driving-scene LiDAR-based perception (including point cloud semantic segmentation, panoptic segmentation and 3D detection, \etc) often project the poin…
Adaptive Surface Normal Constraint for Depth Estimation
Xiaoxiao Long, Cheng Lin, Lingjie Liu +4
We present a novel method for single image depth estimation using surface normal constraints. Existing depth estimation methods either suffer from the lack of geometric constraints…
LEAD: LiDAR Extender for Autonomous Driving
Jianing Zhang, Wei Li, Honggang Gou +2
3D perception using sensors under vehicle industrial standard is the rigid demand in autonomous driving. MEMS LiDAR emerges with irresistible trend due to its lower cost, more robu…
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation
Xinge Zhu, Hui Zhou, Tai Wang +5
State-of-the-art methods for large-scale driving-scene LiDAR segmentation often project the point clouds to 2D space and then process them via 2D convolution. Although this corpora…
Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution
Xibin Song, Yuchao Dai, Dingfu Zhou +4
Despite the remarkable progresses made in deep-learning based depth map super-resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps remains a maj…
AutoRemover: Automatic Object Removal for Autonomous Driving Videos
Rong Zhang, Wei Li, Peng Wang +7
Motivated by the need for photo-realistic simulation in autonomous driving, in this paper we present a video inpainting algorithm \emph{AutoRemover}, designed specifically for gene…