activity
20202023
most citedMonoDistill: Learning Spatial Features for Monocular 3D Object Detection

59 citations · 64 across the 7 of their papers we have counts for

collaborators

10 papers

cs.RO2023

Prediction of SLAM ATE Using an Ensemble Learning Regression Model and 1-D Global Pooling of Data Characterization

Islam Ali, Bingqing, Wan +1

Robustness and resilience of simultaneous localization and mapping (SLAM) are critical requirements for modern autonomous robotic systems. One of the essential steps to achieve rob…

cs.RO2022

Optimizing SLAM Evaluation Footprint Through Dynamic Range Coverage Analysis of Datasets

Islam Ali, Hong Zhang

Simultaneous Localization and Mapping (SLAM) is considered an ever-evolving problem due to its usage in many applications. Evaluation of SLAM is done typically using publicly avail…

cs.RO2022

Following Closely: A Robust Monocular Person Following System for Mobile Robot

Hanjing Ye, Jieting Zhao, Yaling Pan +2

Monocular person following (MPF) is a capability that supports many useful applications of a mobile robot. However, existing MPF solutions are not completely satisfactory. Firstly,…

cs.CV2022★ 59 cited

MonoDistill: Learning Spatial Features for Monocular 3D Object Detection

Zhiyu Chong, Xinzhu Ma, Hong Zhang +4

3D object detection is a fundamental and challenging task for 3D scene understanding, and the monocular-based methods can serve as an economical alternative to the stereo-based or…

cs.RO2021★ 4 cited

Online Mutual Adaptation of Deep Depth Prediction and Visual SLAM

Shing Yan Loo, Moein Shakeri, Sai Hong Tang +2

The ability of accurate depth prediction by a convolutional neural network (CNN) is a major challenge for its wide use in practical visual simultaneous localization and mapping (SL…

cs.CV2020

Full Matching on Low Resolution for Disparity Estimation

Hong Zhang, Shenglun Chen, Zhihui Wang +2

A Multistage Full Matching disparity estimation scheme (MFM) is proposed in this work. We demonstrate that decouple all similarity scores directly from the low-resolution 4D volume…