arXiv:2507.05698v3 Announce Type: replace 
Abstract: Spacecraft pose estimation is crucial for autonomous in-space operations, such as rendezvous, docking and on-orbit servicing. Vision-based pose estimation methods, which typically employ RGB imaging sensors, is a compelling solution for spacecraft pose estimation, but are challenged by harsh lighting conditions, which produce imaging artifacts such as glare, over-exposure, blooming and lens flare. Due to their much higher dynamic range, neuromorphic or event sensors are more resilient to extreme lighting conditions. However, event sensors generally have lower spatial resolution and suffer from reduced signal-to-noise ratio during periods of low relative motion. This work addresses these individual sensor limitations by introducing a sensor fusion approach combining RGB and event sensors. A beam-splitter prism was employed to achieve precise optical and temporal alignment. Then, a RANSAC-based technique was developed to fuse the information from the RGB and event channels to achieve pose estimation that leveraged the strengths of the two modalities. The pipeline was complemented by dropout uncertainty estimation to detect extreme conditions that affect either channel. To benchmark the performance of the proposed event-RGB fusion method, we collected a comprehensive real dataset of RGB and event data for satellite pose estimation in a laboratory setting under a variety of challenging illumination conditions. Encouraging results on the dataset demonstrate the efficacy of our event-RGB fusion approach and further supports the usage of event sensors for spacecraft pose estimation. To support community research on this topic, our dataset has been released publicly.

تسلط دراسة حديثة الضوء على أهمية دمج الأحداث مع RGB لتحسين تقدير وضع المركبات الفضائية، خاصة في ظروف الإضاءة الصعبة. هذه التطورات حيوية لتعزيز العمليات المستقلة في الفضاء، مثل الالتحام وخدمات المدار. من خلال معالجة مشكلات مثل الوهج والتوهج الناتج عن العدسات، يعد هذا النهج المبتكر بجعل المهام الفضائية أكثر أمانًا وكفاءة، مما يمهد الطريق لاستكشافات مستقبلية.

Un estudio reciente destaca la importancia de la fusión de eventos-RGB para mejorar la estimación de la pose de las naves espaciales, especialmente en condiciones de iluminación desafiantes. Este avance es crucial para mejorar las operaciones autónomas en el espacio, como el acoplamiento y el servicio de naves. Al abordar problemas como el deslumbramiento y el destello de lentes, este enfoque innovador promete hacer que las misiones espaciales sean más seguras y eficientes, allanando el camino para futuras exploraciones.

Une étude récente souligne l'importance de la fusion événement-RGB pour améliorer l'estimation de la pose des engins spatiaux, en particulier dans des conditions d'éclairage difficiles. Cette avancée est cruciale pour améliorer les opérations autonomes dans l'espace, telles que les missions d'amarrage et de maintenance. En s'attaquant à des problèmes comme l'éblouissement et le flare des lentilles, cette approche innovante promet de rendre les missions spatiales plus sûres et plus efficaces, ouvrant la voie à de futures explorations.

A recent study highlights the importance of event-RGB fusion for improving spacecraft pose estimation, especially under challenging lighting conditions. This advancement is crucial for enhancing autonomous operations in space, such as docking and servicing missions. By addressing issues like glare and lens flare, this innovative approach promises to make space missions safer and more efficient, paving the way for future exploration.

Event-RGB Fusion for Spacecraft Pose Estimation Under Harsh Lighting

arXiv:2503.02916v2 Announce Type: replace 
Abstract: Localizing a person from a moving monocular camera is critical for Human-Robot Interaction (HRI). To estimate the 3D human position from a 2D image, existing methods either depend on the geometric assumption of a fixed camera or use a position regression model trained on datasets containing little camera ego-motion. These methods are vulnerable to severe camera ego-motion, resulting in inaccurate person localization. We consider person localization as a part of a pose estimation problem. By representing a human with a four-point model, our method jointly estimates the 2D camera attitude and the person's 3D location through optimization. Evaluations on both public datasets and real robot experiments demonstrate our method outperforms baselines in person localization accuracy. Our method is further implemented into a person-following system and deployed on an agile quadruped robot.

تم تطوير طريقة جديدة لتحديد موقع الأشخاص باستخدام كاميرا أحادية تحت حركة الكاميرا، حيث تعالج التحديات المتعلقة بتقدير موضع الشخص ثلاثي الأبعاد بدقة من صور ثنائية الأبعاد تم التقاطها بواسطة كاميرا متحركة. تستخدم هذه الطريقة نموذجًا رباعي النقاط لتقدير وضع الكاميرا ثنائية الأبعاد وموقع الشخص ثلاثي الأبعاد بشكل مشترك، مما يحسن بشكل كبير دقة تحديد الموقع مقارنة بالطرق الحالية.

Se ha desarrollado un nuevo método para la localización de personas monoculares bajo el movimiento de la cámara, abordando los desafíos de estimar con precisión la posición 3D de una persona a partir de imágenes 2D capturadas por una cámara en movimiento. Este enfoque utiliza un modelo de cuatro puntos para estimar conjuntamente la actitud 2D de la cámara y la ubicación 3D de la persona, mejorando significativamente la precisión de la localización en comparación con los métodos existentes.

Une nouvelle méthode de localisation de personnes monoculaires sous le mouvement de la caméra a été développée, abordant les défis d'estimation précise de la position 3D d'une personne à partir d'images 2D capturées par une caméra en mouvement. Cette approche utilise un modèle à quatre points pour estimer conjointement l'attitude 2D de la caméra et la localisation 3D de la personne, améliorant considérablement la précision de localisation par rapport aux méthodes existantes.

A new method for monocular person localization under camera ego-motion has been developed, addressing the challenges of accurately estimating a person's 3D position from 2D images captured by a moving camera. This approach utilizes a four-point model to jointly estimate the camera's 2D attitude and the person's 3D location, significantly improving localization accuracy compared to existing methods.

Monocular Person Localization under Camera Ego-motion

arXiv:2503.21692v4 Announce Type: replace 
Abstract: The integration of multi-view imaging and pose estimation represents a significant advance in computer vision applications, offering new possibilities for understanding human movement and interactions. This work presents a new algorithm that improves multi-view multi-person pose estimation, focusing on fast triangulation speeds and good generalization capabilities. The approach extends to whole-body pose estimation, capturing details from facial expressions to finger movements across multiple individuals and viewpoints. Adaptability to different settings is demonstrated through strong performance across unseen datasets and configurations. To support further progress in this field, all of this work is publicly accessible.

تم تقديم خوارزمية جديدة تُدعى RapidPoseTriangulation، التي تعزز من عملية مثلثات وضع الجسم البشري بالكامل من زوايا متعددة وأشخاص متعددين، مع تحقيق سرعات مثلثات سريعة وقدرات تعميم جيدة. يتيح هذا التقدم التقاط تفاصيل حركة الإنسان، بما في ذلك تعبيرات الوجه وحركات الأصابع، عبر عدة أفراد وزوايا.

Se ha introducido un nuevo algoritmo llamado RapidPoseTriangulation, que mejora la triangulación de pose humana de cuerpo entero en múltiples vistas y personas, logrando velocidades de triangulación rápidas y buenas capacidades de generalización. Este avance permite capturar detalles del movimiento humano, incluidas las expresiones faciales y los movimientos de los dedos, a través de múltiples individuos y puntos de vista.

Un nouvel algorithme nommé RapidPoseTriangulation a été introduit, améliorant la triangulation de pose humaine multi-personnes et multi-vues avec une rapidité et des capacités de généralisation remarquables. Cette avancée permet de capturer en détail les mouvements humains, y compris les expressions faciales et les mouvements des doigts, à travers divers individus et points de vue.

A new algorithm named RapidPoseTriangulation has been introduced, enhancing multi-view multi-person whole-body human pose triangulation with remarkable speed and generalization capabilities. This advancement allows for detailed capture of human movements, including facial expressions and finger movements, across various individuals and viewpoints.

Event-RGB Fusion for Spacecraft Pose Estimation Under Harsh Lighting

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