110 citations · 132 across the 13 of their papers we have counts for
13 papers
Unsupervised Domain Adaptation for Sim-to-Real Object Pose Estimation with Contrastive Alignment and Pseudo-Label Refinement
Nidhal Eddine Chenni, Arunkumar Rathinam, Djamila Aouada
Unsupervised domain adaptation (UDA) enables robust transfer of knowledge from simulated to real environments while exploiting a subset of unlabeled target data to improve real-wor…
TALON: Token-Aligned Lightweight Adapters for 6-DoF Spacecraft Pose Estimation
Abid Ali, Arunkumar Rathinam, Djamila Aouada
Monocular 6-DoF spacecraft pose estimation methods predominantly process individual frames, discarding the temporal information present in an image sequence acquired during spacecr…
Efficient Onboard Spacecraft Pose Estimation with Event Cameras and Neuromorphic Hardware
Arunkumar Rathinam, Jules Lecomte, Jost Reelsen +3
Reliable relative pose estimation is a key enabler for autonomous rendezvous and proximity operations, yet space imagery is notoriously challenging due to extreme illumination, hig…
Annotation Free Spacecraft Detection and Segmentation using Vision Language Models
Samet Hicsonmez, Jose Sosa, Dan Pineau +4
Vision Language Models (VLMs) have demonstrated remarkable performance in open-world zero-shot visual recognition. However, their potential in space-related applications remains la…
Domain Adaptive Object Detection for Space Applications with Real-Time Constraints
Samet Hicsonmez, Abd El Rahman Shabayek, Arunkumar Rathinam +1
Object detection is essential in space applications targeting Space Domain Awareness and also applications involving relative navigation scenarios. Current deep learning models for…
Bridging the Synthetic-Real Gap: Supervised Domain Adaptation for Robust Spacecraft 6-DoF Pose Estimation
Inder Pal Singh, Nidhal Eddine Chenni, Abd El Rahman Shabayek +2
Spacecraft Pose Estimation (SPE) is a fundamental capability for autonomous space operations such as rendezvous, docking, and in-orbit servicing. Hybrid pipelines that combine obje…
Motion Aware ViT-based Framework for Monocular 6-DoF Spacecraft Pose Estimation
Jose Sosa, Dan Pineau, Arunkumar Rathinam +2
Monocular 6-DoF pose estimation plays an important role in multiple spacecraft missions. Most existing pose estimation approaches rely on single images with static keypoint localis…
Hybrid Attention for Robust RGB-T Pedestrian Detection in Real-World Conditions
Arunkumar Rathinam, Leo Pauly, Abd El Rahman Shabayek +4
Multispectral pedestrian detection has gained significant attention in recent years, particularly in autonomous driving applications. To address the challenges posed by adversarial…
SPADES: A Realistic Spacecraft Pose Estimation Dataset using Event Sensing
Arunkumar Rathinam, Haytam Qadadri, Djamila Aouada
In recent years, there has been a growing demand for improved autonomy for in-orbit operations such as rendezvous, docking, and proximity maneuvers, leading to increased interest i…
A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects
Leo Pauly, Wassim Rharbaoui, Carl Shneider +3
Estimating the pose of an uncooperative spacecraft is an important computer vision problem for enabling the deployment of automatic vision-based systems in orbit, with applications…
3D-Aware Object Localization using Gaussian Implicit Occupancy Function
Vincent Gaudillière, Leo Pauly, Arunkumar Rathinam +3
To automatically localize a target object in an image is crucial for many computer vision applications. To represent the 2D object, ellipse labels have recently been identified as…
Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification
Indel Pal Singh, Enjie Ghorbel, Anis Kacem +2
In this paper, a discriminator-free adversarial-based Unsupervised Domain Adaptation (UDA) for Multi-Label Image Classification (MLIC) referred to as DDA-MLIC is proposed. Recently…
Lessons from a Space Lab -- An Image Acquisition Perspective
Leo Pauly, Michele Lynn Jamrozik, Miguel Ortiz Del Castillo +10
The use of Deep Learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for tr…