Search Results for author: Mai Bui

Found 6 papers, 2 papers with code

DistillPose: Lightweight Camera Localization Using Auxiliary Learning

no code implementations9 Aug 2021 Yehya Abouelnaga, Mai Bui, Slobodan Ilic

A siamese convolutional neural network regresses the relative pose from the nearest neighboring database image to the query image.

Auxiliary Learning Camera Localization +2

Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation

1 code implementation20 Dec 2020 Haowen Deng, Mai Bui, Nassir Navab, Leonidas Guibas, Slobodan Ilic, Tolga Birdal

For the former we contributed our own dataset composed of five indoor scenes where it is unavoidable to capture images corresponding to views that are hard to uniquely identify.

Camera Relocalization Pose Estimation

6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference

2 code implementations ECCV 2020 Mai Bui, Tolga Birdal, Haowen Deng, Shadi Albarqouni, Leonidas Guibas, Slobodan Ilic, Nassir Navab

We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses.

Camera Relocalization

Adversarial Networks for Camera Pose Regression and Refinement

no code implementations15 Mar 2019 Mai Bui, Christoph Baur, Nassir Navab, Slobodan Ilic, Shadi Albarqouni

Despite recent advances on the topic of direct camera pose regression using neural networks, accurately estimating the camera pose of a single RGB image still remains a challenging task.

Pose Estimation

Scene Coordinate and Correspondence Learning for Image-Based Localization

no code implementations22 May 2018 Mai Bui, Shadi Albarqouni, Slobodan Ilic, Nassir Navab

Scene coordinate regression has become an essential part of current camera re-localization methods.

Image-Based Localization

When Regression Meets Manifold Learning for Object Recognition and Pose Estimation

no code implementations16 May 2018 Mai Bui, Sergey Zakharov, Shadi Albarqouni, Slobodan Ilic, Nassir Navab

By combining the strengths of manifold learning using triplet loss and pose regression, we could either estimate the pose directly reducing the complexity compared to NN search, or use learned descriptor for the NN descriptor matching.

Multi-Task Learning Object Recognition +2

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