300 citations · 532 across the 12 of their papers we have counts for
12 papers
Improved 3D Gaussian Splatting of Unknown Spacecraft Structure Using Space Environment Illumination Knowledge
Tae Ha Park, Simone D'Amico
This work presents a novel pipeline to recover the 3D structure of an unknown target spacecraft from a sequence of images captured during Rendezvous and Proximity Operations (RPO)…
Bridging the Domain Gap for Flight-Ready Spaceborne Vision
Tae Ha Park, Simone D'Amico
This work presents Spacecraft Pose Network v3 (SPNv3), a Neural Network (NN) for monocular pose estimation of a known, non-cooperative target spacecraft. SPNv3 is designed and trai…
Event-based Structure-from-Orbit
Ethan Elms, Yasir Latif, Tae Ha Park +1
Event sensors offer high temporal resolution visual sensing, which makes them ideal for perceiving fast visual phenomena without suffering from motion blur. Certain applications in…
Online Supervised Training of Spaceborne Vision during Proximity Operations using Adaptive Kalman Filtering
Tae Ha Park, Simone D'Amico
This work presents an Online Supervised Training (OST) method to enable robust vision-based navigation about a non-cooperative spacecraft. Spaceborne Neural Networks (NN) are susce…
Adaptive Neural Network-based Unscented Kalman Filter for Robust Pose Tracking of Noncooperative Spacecraft
Tae Ha Park, Simone D'Amico
This paper presents a neural network-based Unscented Kalman Filter (UKF) to estimate and track the pose (i.e., position and orientation) of a known, noncooperative, tumbling target…
Robust Multi-Task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gap
Tae Ha Park, Simone D'Amico
This work presents Spacecraft Pose Network v2 (SPNv2), a Convolutional Neural Network (CNN) for pose estimation of noncooperative spacecraft across domain gap. SPNv2 is a multi-sca…
SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap
Tae Ha Park, Marcus Märtens, Gurvan Lecuyer +2
Autonomous vision-based spaceborne navigation is an enabling technology for future on-orbit servicing and space logistics missions. While computer vision in general has benefited f…
Robotic Testbed for Rendezvous and Optical Navigation: Multi-Source Calibration and Machine Learning Use Cases
Tae Ha Park, Juergen Bosse, Simone D'Amico
This work presents the most recent advances of the Robotic Testbed for Rendezvous and Optical Navigation (TRON) at Stanford University - the first robotic testbed capable of valida…
Auxiliary Class Based Multiple Choice Learning
Sihwan Kim, Dae Yon Jung, Taejang Park
The merit of ensemble learning lies in having different outputs from many individual models on a single input, i.e., the diversity of the base models. The high quality of diversity…
Learning Robust Feature Representations for Scene Text Detection
Sihwan Kim, Taejang Park
Scene text detection based on deep neural networks have progressed substantially over the past years. However, previous state-of-the-art methods may still fall short when dealing w…
Satellite Pose Estimation Challenge: Dataset, Competition Design and Results
Mate Kisantal, Sumant Sharma, Tae Ha Park +3
Reliable pose estimation of uncooperative satellites is a key technology for enabling future on-orbit servicing and debris removal missions. The Kelvins Satellite Pose Estimation C…
Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft
Tae Ha Park, Sumant Sharma, Simone D'Amico
This work presents a novel Convolutional Neural Network (CNN) architecture and a training procedure to enable robust and accurate pose estimation of a noncooperative spacecraft. Fi…