129 citations · 307 across the 13 of their papers we have counts for
15 papers
Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes
Duy-Tho Le, Trung Pham, Jianfei Cai +1
Optimizing the similarity between parametric shapes is crucial for numerous computer vision tasks, where Intersection over Union (IoU) stands as the canonical measure. However, exi…
DPPD: Deformable Polar Polygon Object Detection
Yang Zheng, Oles Andrienko, Yonglei Zhao +2
Regular object detection methods output rectangle bounding boxes, which are unable to accurately describe the actual object shapes. Instance segmentation methods output pixel-level…
Ensemble approximate control variate estimators: Applications to multi-fidelity importance sampling
Trung Pham, Alex A. Gorodetsky
The recent growth in multi-fidelity uncertainty quantification has given rise to a large set of variance reduction techniques that leverage information from model ensembles to prov…
Bayesian Semantic Instance Segmentation in Open Set World
Trung Pham, Vijay Kumar B G, Thanh-Toan Do +2
This paper addresses the semantic instance segmentation task in the open-set conditions, where input images can contain known and unknown object classes. The training process of ex…
Structure Aware SLAM using Quadrics and Planes
Mehdi Hosseinzadeh, Yasir Latif, Trung Pham +2
Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While point-based SLAM methods provide accurate camera localization, the generated maps la…
Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image
Thanh-Toan Do, Ming Cai, Trung Pham +1
Detecting objects and their 6D poses from only RGB images is an important task for many robotic applications. While deep learning methods have made significant progress in visual o…
Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation
Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan +4
Learning compact binary codes for image retrieval task using deep neural networks has attracted increasing attention recently. However, training deep hashing networks for the task…
A Bayesian Data Augmentation Approach for Learning Deep Models
Toan Tran, Trung Pham, Gustavo Carneiro +2
Data augmentation is an essential part of the training process applied to deep learning models. The motivation is that a robust training process for deep learning models depends on…
Design of a Multi-Modal End-Effector and Grasping System: How Integrated Design helped win the Amazon Robotics Challenge
S. Wade-McCue, N. Kelly-Boxall, M. McTaggart +19
We present the grasping system and design approach behind Cartman, the winning entrant in the 2017 Amazon Robotics Challenge. We investigate the design processes leading up to the…
Semantic Segmentation from Limited Training Data
A. Milan, T. Pham, K. Vijay +22
We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transpa…
SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes
Trung Pham, Thanh-Toan Do, Niko Sünderhauf +1
This paper presents SceneCut, a novel approach to jointly discover previously unseen objects and non-object surfaces using a single RGB-D image. SceneCut's joint reasoning over sce…
Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge
D. Morrison, A. W. Tow, M. McTaggart +19
The Amazon Robotics Challenge enlisted sixteen teams to each design a pick-and-place robot for autonomous warehousing, addressing development in robotic vision and manipulation. Th…
Simultaneous Feature Aggregating and Hashing for Large-scale Image Search
Thanh-Toan Do, Dang-Khoa Le Tan, Trung T. Pham +1
In most state-of-the-art hashing-based visual search systems, local image descriptors of an image are first aggregated as a single feature vector. This feature vector is then subje…
Meaningful Maps With Object-Oriented Semantic Mapping
Niko Sünderhauf, Trung T. Pham, Yasir Latif +2
For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them. The maj…
The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research
Jürgen Leitner, Adam W. Tow, Jake E. Dean +15
Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress. They make research comparable…