activity
20202023
most citedProbabilistic and Geometric Depth: Detecting Objects in Perspective

95 citations · 316 across the 17 of their papers we have counts for

collaborators
Showing 2021 · cs.CVShow all

5 papers · 2 filters

cs.CV2021★ 69 cited

Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion

Tong Wu, Liang Pan, Junzhe Zhang +3

Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted metrics for measuring the similarity between two point sets. However, CD is usually insensitive to mi…

cs.CV2021★ 1 cited

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…

cs.CV2021★ 1 cited

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…

cs.CV2021★ 95 cited

Probabilistic and Geometric Depth: Detecting Objects in Perspective

Tai Wang, Xinge Zhu, Jiangmiao Pang +1

3D object detection is an important capability needed in various practical applications such as driver assistance systems. Monocular 3D detection, as a representative general setti…

cs.CV2021★ 27 cited

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…