activity
20192021
most citedA Spacecraft Dataset for Detection, Segmentation and Parts Recognition

6 citations · 12 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV20211 cited

Occlusion-Robust Object Pose Estimation with Holistic Representation

Bo Chen, Tat-Jun Chin, Marius Klimavicius

Practical object pose estimation demands robustness against occlusions to the target object. State-of-the-art (SOTA) object pose estimators take a two-stage approach, where the fir…

cs.CV20215 cited

Physical Adversarial Attacks on an Aerial Imagery Object Detector

Andrew Du, Bo Chen, Tat-Jun Chin +4

Deep neural networks (DNNs) have become essential for processing the vast amounts of aerial imagery collected using earth-observing satellite platforms. However, DNNs are vulnerabl…

cs.CV20216 cited

A Spacecraft Dataset for Detection, Segmentation and Parts Recognition

Dung Anh Hoang, Bo Chen, Tat-Jun Chin

Virtually all aspects of modern life depend on space technology. Thanks to the great advancement of computer vision in general and deep learning-based techniques in particular, ove…

cs.CV2020

Topological Sweep for Multi-Target Detection of Geostationary Space Objects

Daqi Liu, Bo Chen, Tat-Jun Chin +1

Conducting surveillance of the Earth's orbit is a key task towards achieving space situational awareness (SSA). Our work focuses on the optical detection of man-made objects (e.g.,…

cs.CV2019

End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization

Bo Chen, Alvaro Parra, Jiewei Cao +2

Deep networks excel in learning patterns from large amounts of data. On the other hand, many geometric vision tasks are specified as optimization problems. To seamlessly combine de…

cs.CV2019

Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement

Bo Chen, Jiewei Cao, Alvaro Parra +1

We propose an approach to estimate the 6DOF pose of a satellite, relative to a canonical pose, from a single image. Such a problem is crucial in many space proximity operations, su…