activity
20182022
most citedDF-VO: What Should Be Learnt for Visual Odometry?

27 citations · 54 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV202220 cited

ActiveRMAP: Radiance Field for Active Mapping And Planning

Huangying Zhan, Jiyang Zheng, Yi Xu +2

A high-quality 3D reconstruction of a scene from a collection of 2D images can be achieved through offline/online mapping methods. In this paper, we explore active mapping from the…

cs.RO2022

Predicting Topological Maps for Visual Navigation in Unexplored Environments

Huangying Zhan, Hamid Rezatofighi, Ian Reid

We propose a robotic learning system for autonomous exploration and navigation in unexplored environments. We are motivated by the idea that even an unseen environment may be famil…

cs.CV202127 cited

DF-VO: What Should Be Learnt for Visual Odometry?

Huangying Zhan, Chamara Saroj Weerasekera, Jia-Wang Bian +2

Multi-view geometry-based methods dominate the last few decades in monocular Visual Odometry for their superior performance, while they have been vulnerable to dynamic and low-text…

cs.CV2019

Visual Odometry Revisited: What Should Be Learnt?

Huangying Zhan, Chamara Saroj Weerasekera, Jiawang Bian +1

In this work we present a monocular visual odometry (VO) algorithm which leverages geometry-based methods and deep learning. Most existing VO/SLAM systems with superior performance…

cs.CV2019

Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video

Jia-Wang Bian, Zhichao Li, Naiyan Wang +4

Recent work has shown that CNN-based depth and ego-motion estimators can be learned using unlabelled monocular videos. However, the performance is limited by unidentified moving ob…

cs.CV20197 cited

Self-supervised Learning for Single View Depth and Surface Normal Estimation

Huangying Zhan, Chamara Saroj Weerasekera, Ravi Garg +1

In this work we present a self-supervised learning framework to simultaneously train two Convolutional Neural Networks (CNNs) to predict depth and surface normals from a single ima…