34 citations · 46 across the 5 of their papers we have counts for
7 papers
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…
SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation
Xidong Peng, Xinge Zhu, Tai Wang +1
3D detection plays an indispensable role in environment perception. Due to the high cost of commonly used LiDAR sensor, stereo vision based 3D detection, as an economical yet effec…
FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection
Tai Wang, Xinge Zhu, Jiangmiao Pang +1
Monocular 3D object detection is an important task for autonomous driving considering its advantage of low cost. It is much more challenging than conventional 2D cases due to its i…
FLAVA: Find, Localize, Adjust and Verify to Annotate LiDAR-Based Point Clouds
Tai Wang, Conghui He, Zhe Wang +2
Recent years have witnessed the rapid progress of perception algorithms on top of LiDAR, a widely adopted sensor for autonomous driving systems. These LiDAR-based solutions are typ…
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…
SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
Xinge Zhu, Yuexin Ma, Tai Wang +3
Multi-class 3D object detection aims to localize and classify objects of multiple categories from point clouds. Due to the nature of point clouds, i.e. unstructured, sparse and noi…