Arunkumar Rathinam — accepted AI-conference papers

2026

Annotation Free Spacecraft Detection and Segmentation Using Vision Language Models

Samet Hicsonmez, Jose Sosa, Dan Pineau, Inder Pal Singh, Arunkumar Rathinam, Abd El Rahman Shabayek +1

ICRA 2026poster

Vision Language Models (VLMs) have demonstrated remarkable performance in open-world zero-shot visual recognition. However, their potential in space-related applications remains largely unexplored. In the space domain, accurate manual annotation is particularly challenging due to factors such as low…

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2025

Hybrid Attention for Robust RGB-T Pedestrian Detection in Real-World Conditions

Arunkumar Rathinam, Leo Pauly, Abd El Rahman Shabayek, Wassim Rharbaoui, Anis Kacem, Vincent Gaudillière +1

RA-L 2025

Multispectral pedestrian detection has gained significant attention in recent years, particularly in autonomous driving applications. To address the challenges posed by adversarial illumination conditions, the combination of thermal and visible images has demonstrated its advantages. However, existi

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2024

SPADES: A Realistic Spacecraft Pose Estimation Dataset using Event Sensing

Arunkumar Rathinam, Haytam Qadadri, Djamila Aouada

ICRA 2024poster

In recent years, there has been a growing demand for improved autonomy for in-orbit operations such as rendezvous, docking, and proximity manoeuvres, leading to increased interest in employing Deep Learning-based Spacecraft Pose Estimation techniques. However, due to limited access to real target da…

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2023

3D-Aware Object Localization using Gaussian Implicit Occupancy Function

Vincent Gaudillière, Leo Pauly, Arunkumar Rathinam, Albert Garcia Sanchez, Mohamed Adel Musallam, Djamila Aouada

IROS 2023poster

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 a promising alternative to axis-aligned bounding boxes. This paper further considers 3D-aware ellipse labels, i.e., elli…

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