Search Results for author: Huangying Zhan

Found 8 papers, 6 papers with code

Unsupervised Scale-consistent Depth Learning from Video

2 code implementations25 May 2021 Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Zhichao Li, Le Zhang, Chunhua Shen, Ming-Ming Cheng, Ian Reid

We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time.

Monocular Depth Estimation Monocular Visual Odometry +1

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

2 code implementations1 Mar 2021 Huangying Zhan, Chamara Saroj Weerasekera, Jia-Wang Bian, Ravi Garg, Ian Reid

More surprisingly, they show that the well-trained networks enable scale-consistent predictions over long videos, while the accuracy is still inferior to traditional methods because of ignoring geometric information.

Monocular Visual Odometry Optical Flow Estimation

Auto-Rectify Network for Unsupervised Indoor Depth Estimation

1 code implementation4 Jun 2020 Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Tat-Jun Chin, Chunhua Shen, Ian Reid

However, excellent results have mostly been obtained in street-scene driving scenarios, and such methods often fail in other settings, particularly indoor videos taken by handheld devices.

Monocular Depth Estimation Self-Supervised Learning +1

Visual Odometry Revisited: What Should Be Learnt?

2 code implementations21 Sep 2019 Huangying Zhan, Chamara Saroj Weerasekera, Jia-Wang Bian, Ian Reid

In this work we present a monocular visual odometry (VO) algorithm which leverages geometry-based methods and deep learning.

Monocular Visual Odometry

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

2 code implementations NeurIPS 2019 Jia-Wang Bian, Zhichao Li, Naiyan Wang, Huangying Zhan, Chunhua Shen, Ming-Ming Cheng, Ian Reid

To the best of our knowledge, this is the first work to show that deep networks trained using unlabelled monocular videos can predict globally scale-consistent camera trajectories over a long video sequence.

Ranked #35 on Monocular Depth Estimation on KITTI Eigen split (using extra training data)

Depth And Camera Motion Monocular Depth Estimation +1

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

no code implementations1 Mar 2019 Huangying Zhan, Chamara Saroj Weerasekera, Ravi Garg, Ian Reid

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 image.

Depth Prediction Monocular Depth Estimation +2

Efficient Dense Point Cloud Object Reconstruction using Deformation Vector Fields

no code implementations ECCV 2018 Kejie Li, Trung Pham, Huangying Zhan, Ian Reid

Given a single image at an arbitrary viewpoint, a CNN predicts multiple surfaces, each in a canonical location relative to the object.

3D Object Reconstruction

Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction

1 code implementation CVPR 2018 Huangying Zhan, Ravi Garg, Chamara Saroj Weerasekera, Kejie Li, Harsh Agarwal, Ian Reid

Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner.

Depth And Camera Motion Depth Prediction +2

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