activity
20182024
most citedWhen Regression Meets Manifold Learning for Object Recognition and Pose Estimation

30 citations · 66 across the 7 of their papers we have counts for

collaborators

7 papers

eess.IV2024★ 8 cited

Real-time guidewire tracking and segmentation in intraoperative x-ray

Baochang Zhang, Mai Bui, Cheng Wang +3

During endovascular interventions, physicians have to perform accurate and immediate operations based on the available real-time information, such as the shape and position of guid…

cs.CV2021

DistillPose: Lightweight Camera Localization Using Auxiliary Learning

Yehya Abouelnaga, Mai Bui, Slobodan Ilic

We propose a lightweight retrieval-based pipeline to predict 6DOF camera poses from RGB images. Our pipeline uses a convolutional neural network (CNN) to encode a query image as a…

cs.CV2020★ 6 cited

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

Haowen Deng, Mai Bui, Nassir Navab +3

In this work, we introduce Deep Bingham Networks (DBN), a generic framework that can naturally handle pose-related uncertainties and ambiguities arising in almost all real life app…

cs.CV2020★ 1 cited

6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference

Mai Bui, Tolga Birdal, Haowen Deng +4

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

cs.CV2019★ 2 cited

Adversarial Networks for Camera Pose Regression and Refinement

Mai Bui, Christoph Baur, Nassir Navab +2

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…

cs.CV2018★ 30 cited

When Regression Meets Manifold Learning for Object Recognition and Pose Estimation

Mai Bui, Sergey Zakharov, Shadi Albarqouni +2

In this work, we propose a method for object recognition and pose estimation from depth images using convolutional neural networks. Previous methods addressing this problem rely on…