85 citations · 163 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors
Chamara Saroj Weerasekera, Thanuja Dharmasiri, Ravi Garg +2
We present ``just-in-time reconstruction" as real-time image-guided inpainting of a map with arbitrary scale and sparsity to generate a fully dense depth map for the image. In part…
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
Huangying Zhan, Ravi Garg, Chamara Saroj Weerasekera +3
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. Recen…
Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction
Chamara Saroj Weerasekera, Ravi Garg, Yasir Latif +1
Visual SLAM (Simultaneous Localization and Mapping) methods typically rely on handcrafted visual features or raw RGB values for establishing correspondences between images. These f…