no code implementations • 11 May 2018 • Chamara Saroj Weerasekera, Thanuja Dharmasiri, Ravi Garg, Tom Drummond, Ian Reid
Crucially, we obtain the confidence weights that parameterize the CRF model in a data-dependent manner via Convolutional Neural Networks (CNNs) which are trained to model the conditional depth error distributions given each source of input depth map and the associated RGB image.
no code implementations • 16 Nov 2017 • Chamara Saroj Weerasekera, Ravi Garg, Yasir Latif, Ian Reid
Visual SLAM (Simultaneous Localization and Mapping) methods typically rely on handcrafted visual features or raw RGB values for establishing correspondences between images.
no code implementations • 6 Sep 2016 • Ravi Garg, Shu Dong, Sanjiv Shah, Siddhartha R. Jonnalagadda
Rare diseases are very difficult to identify among large number of other possible diagnoses.
no code implementations • 16 Mar 2016 • Ravi Garg, Anders Eriksson, Ian Reid
Additionally, we evaluate our method on the challenging problem of Non-Rigid Structure from Motion and our approach delivers promising results on CMU mocap dataset despite the presence of significant occlusions and noise.
no code implementations • 29 Nov 2018 • Kejie Li, Ravi Garg, Ming Cai, Ian Reid
3D shape reconstruction from a single image is a highly ill-posed problem.
no code implementations • CVPR 2013 • Ravi Garg, Anastasios Roussos, Lourdes Agapito
This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence.
no code implementations • 1 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.
Ranked #62 on Monocular Depth Estimation on KITTI Eigen split
no code implementations • 16 May 2019 • Rajhans Singh, Pavan Turaga, Suren Jayasuriya, Ravi Garg, Martin W. Braun
The advent of generative adversarial networks (GAN) has enabled new capabilities in synthesis, interpolation, and data augmentation heretofore considered very challenging.
no code implementations • 7 Nov 2019 • Carlos Lassance, Yasir Latif, Ravi Garg, Vincent Gripon, Ian Reid
One solution to this problem is to learn a deep neural network to infer the pose of a query image after learning on a dataset of images with known poses.
no code implementations • 3 May 2021 • Ravi Garg, Adi Hajj-Ahmad, Min Wu
In this study, we demonstrate that it is possible to pinpoint the location-of-recording to a certain geographical resolution using power signal recordings containing strong ENF traces.
no code implementations • CVPR 2022 • Alexander Long, Wei Yin, Thalaiyasingam Ajanthan, Vu Nguyen, Pulak Purkait, Ravi Garg, Alan Blair, Chunhua Shen, Anton Van Den Hengel
We introduce Retrieval Augmented Classification (RAC), a generic approach to augmenting standard image classification pipelines with an explicit retrieval module.
Ranked #4 on Long-tail Learning on iNaturalist 2018
no code implementations • ICCV 2023 • Peixia Li, Pulak Purkait, Thalaiyasingam Ajanthan, Majid Abdolshah, Ravi Garg, Hisham Husain, Chenchen Xu, Stephen Gould, Wanli Ouyang, Anton Van Den Hengel
Each learning group consists of a teacher network, a student network and a novel filter module.
1 code implementation • 26 Sep 2017 • Yasir Latif, Ravi Garg, Michael Milford, Ian Reid
In the process, meaningful feature spaces are learned for each domain, the distances in which can be used for the task of place recognition.
Robotics
1 code implementation • 17 Jul 2017 • Pan Ji, Ian Reid, Ravi Garg, Hongdong Li, Mathieu Salzmann
In this paper, we present a kernel subspace clustering method that can handle non-linear models.
2 code implementations • 16 Mar 2016 • Ravi Garg, Vijay Kumar BG, Gustavo Carneiro, Ian Reid
In this work we propose a unsupervised framework to learn a deep convolutional neural network for single view depth predic- tion, without requiring a pre-training stage or annotated ground truth depths.
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.
2 code implementations • 1 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.