arXiv:2511.14107v1 Announce Type: new 
Abstract: Depth information is crucial for autonomous driving and intelligent robot navigation. The simplicity and flexibility of self-supervised monocular depth estimation are conducive to its role in these fields. However, most existing monocular depth estimation models consume many computing resources. Although some methods have reduced the model's size and improved computing efficiency, the performance deteriorates, seriously hindering the real-world deployment of self-supervised monocular depth estimation models in the real world. To address this problem, we proposed a real-time self-supervised monocular depth estimation method and implemented it in the real world. It is called RTS-Mono, which is a lightweight and efficient encoder-decoder architecture. The encoder is based on Lite-Encoder, and the decoder is designed with a multi-scale sparse fusion framework to minimize redundancy, ensure performance, and improve inference speed. RTS-Mono achieved state-of-the-art (SoTA) performance in high and low resolutions with extremely low parameter counts (3 M) in experiments based on the KITTI dataset. Compared with lightweight methods, RTS-Mono improved Abs Rel and Sq Rel by 5.6% and 9.8% at low resolution and improved Sq Rel and RMSE by 6.1% and 1.9% at high resolution. In real-world deployment experiments, RTS-Mono has extremely high accuracy and can perform real-time inference on Nvidia Jetson Orin at a speed of 49 FPS. Source code is available at https://github.com/ZYCheng777/RTS-Mono.

RTS-Mono هو طريقة جديدة لتقدير العمق الأحادي الذاتي الإشراف في الوقت الحقيقي، تهدف إلى تحسين القيادة الذاتية والتنقل الذكي للروبوتات. غالبًا ما تتطلب نماذج تقدير العمق الأحادي التقليدية موارد حوسبة كبيرة، مما يمكن أن يعيق تطبيقها العملي. RTS-Mono يعالج هذه المشكلة من خلال بنية خفيفة الوزن من نوع مشفر-مفكك، تستخدم مشفرًا خفيفًا وإطار دمج متناثر متعدد المقاييس لتحسين الأداء وسرعة الاستدلال، مما يجعلها مناسبة للنشر في العالم الحقيقي.

RTS-Mono es un nuevo método de estimación de profundidad monocular auto-supervisada en tiempo real, destinado a mejorar la conducción autónoma y la navegación robótica inteligente. Los modelos tradicionales de estimación de profundidad monocular suelen requerir muchos recursos informáticos, lo que puede obstaculizar su aplicación práctica. RTS-Mono aborda este problema con una arquitectura ligera de codificador-decodificador, utilizando un Lite-Encoder y un marco de fusión dispersa a múltiples escalas para optimizar el rendimiento y la velocidad de inferencia, haciéndolo adecuado para el despl…

RTS-Mono est une nouvelle méthode d'estimation de profondeur monoculaire auto-supervisée en temps réel, visant à améliorer la conduite autonome et la navigation robotique intelligente. Les modèles traditionnels d'estimation de profondeur monoculaire nécessitent souvent d'importantes ressources informatiques, ce qui peut freiner leur application pratique. RTS-Mono résout ce problème grâce à une architecture légère d'encodeur-décodeur, utilisant un Lite-Encoder et un cadre de fusion sparse multi-échelle pour optimiser les performances et la vitesse d'inférence, la rendant adaptée à un déploiemen…

RTS-Mono is a newly proposed real-time self-supervised monocular depth estimation method aimed at enhancing autonomous driving and intelligent robot navigation. Traditional monocular depth estimation models often require significant computing resources, which can hinder their practical application. RTS-Mono addresses this issue with a lightweight encoder-decoder architecture, utilizing a Lite-Encoder and a multi-scale sparse fusion framework to optimize performance and inference speed, making it suitable for real-world deployment.

RTS-Mono: A Real-Time Self-Supervised Monocular Depth Estimation Method for Real-World Deployment

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