arXiv:2510.14270v2 Announce Type: replace 
Abstract: Scene reconstruction has emerged as a central challenge in computer vision, with approaches such as Neural Radiance Fields (NeRF) and Gaussian Splatting achieving remarkable progress. While Gaussian Splatting demonstrates strong performance on large-scale datasets, it often struggles to capture fine details or maintain realism in regions with sparse coverage, largely due to the inherent limitations of sparse 3D training data.
  In this work, we propose GauSSmart, a hybrid method that effectively bridges 2D foundational models and 3D Gaussian Splatting reconstruction. Our approach integrates established 2D computer vision techniques, including convex filtering and semantic feature supervision from foundational models such as DINO, to enhance Gaussian-based scene reconstruction. By leveraging 2D segmentation priors and high-dimensional feature embeddings, our method guides the densification and refinement of Gaussian splats, improving coverage in underrepresented areas and preserving intricate structural details.
  We validate our approach across three datasets, where GauSSmart consistently outperforms existing Gaussian Splatting in the majority of evaluated scenes. Our results demonstrate the significant potential of hybrid 2D-3D approaches, highlighting how the thoughtful combination of 2D foundational models with 3D reconstruction pipelines can overcome the limitations inherent in either approach alone.

تحدث GauSSmart ضجة في مجال رؤية الكمبيوتر من خلال تحسين تقنيات إعادة البناء ثلاثي الأبعاد باستخدام نماذج الأساس ثنائية الأبعاد والتصفية الهندسية. تعالج هذه التطورات التحديات الشائعة التي تواجه الأساليب الحالية مثل حقول الإشعاع العصبي والتفريغ الغاوسي، خاصة في التقاط التفاصيل الدقيقة والحفاظ على الواقعية في المناطق ذات البيانات النادرة. من خلال تحسين الدقة والتفاصيل في إعادة بناء المشاهد، لا يدفع GauSSmart حدود التكنولوجيا فحسب، بل يفتح أيضًا آفاقًا جديدة للتطبيقات في مختلف الصناعات، مما يجعله تطورًا مهمًا يستحق المتابعة.

GauSSmart está causando revuelo en el campo de la visión por computadora al mejorar las técnicas de reconstrucción 3D mediante el uso innovador de modelos de base 2D y filtrado geométrico. Este avance aborda los desafíos comunes que enfrentan métodos existentes como los Campos de Radiancia Neuronal y el Splatting Gaussiano, especialmente en la captura de detalles finos y el mantenimiento del realismo en áreas de datos escasos. Al mejorar la precisión y el detalle de la reconstrucción de escenas, GauSSmart no solo empuja los límites de la tecnología, sino que también abre nuevas posibilidades para aplicaciones en diversas industrias, lo que lo convierte en un desarrollo significativo que vale la pena seguir.

GauSSmart fait sensation dans le domaine de la vision par ordinateur en améliorant les techniques de reconstruction 3D grâce à l'utilisation innovante de modèles de fondation 2D et de filtrage géométrique. Cette avancée répond aux défis courants rencontrés par les méthodes existantes telles que les champs de radiance neuronaux et le splatting gaussien, en particulier pour capturer les détails fins et maintenir le réalisme dans les zones de données rares. En améliorant la précision et le détail de la reconstruction de scènes, GauSSmart repousse non seulement les limites de la technologie, mais ouvre également de nouvelles possibilités d'applications dans divers secteurs, ce qui en fait un développement significatif à suivre.

GauSSmart is making waves in the field of computer vision by enhancing 3D reconstruction techniques through innovative use of 2D foundation models and geometric filtering. This advancement addresses the common challenges faced by existing methods like Neural Radiance Fields and Gaussian Splatting, particularly in capturing fine details and maintaining realism in sparse data areas. By improving the accuracy and detail of scene reconstruction, GauSSmart not only pushes the boundaries of technology but also opens up new possibilities for applications in various industries, making it a significant development worth following.

GauSSmart: Enhanced 3D Reconstruction through 2D Foundation Models and Geometric Filtering

arXiv:2601.07982v1 Announce Type: new 
Abstract: We develop a new statistical ideal observer model that performs holistic visual search (or gist) processing in part by placing thresholds on minimum extractable image features. In this model, the ideal observer reduces the number of free parameters thereby shrinking down the system. The applications of this novel framework is in medical image perception (for optimizing imaging systems and algorithms), computer vision, benchmarking performance and enabling feature selection/evaluations. Other applications are in target detection and recognition in defense/security as well as evaluating sensors and detectors.

تم تطوير نموذج جديد للمراقب المثالي الإحصائي لتحسين معالجة البحث البصري الشامل من خلال وضع حدود على الميزات القابلة للاستخراج من الصورة. يهدف هذا النموذج إلى تبسيط النظام من خلال تقليل المعلمات الحرة، مع تطبيقات في إدراك الصور الطبية ورؤية الكمبيوتر والدفاع/الأمن.

Se ha desarrollado un nuevo modelo estadístico de observador ideal para mejorar el procesamiento de búsqueda visual holística al establecer umbrales sobre las características de imagen mínimas extraíbles. Este modelo busca optimizar el sistema reduciendo los parámetros libres, con aplicaciones en percepción de imágenes médicas, visión por computadora y defensa/seguridad.

Un nouveau modèle d'observateur idéal statistique a été développé pour améliorer le traitement de la recherche visuelle holistique en établissant des seuils sur les caractéristiques d'image minimales extractibles. Ce modèle vise à rationaliser le système en réduisant les paramètres libres, avec des applications dans la perception d'images médicales, la vision par ordinateur et la défense/sécurité.

A new statistical ideal observer model has been developed to enhance holistic visual search processing by establishing thresholds on minimum extractable image features. This model aims to streamline the system by reducing free parameters, with applications in medical image perception, computer vision, and defense/security.

Likelihood ratio for a binary Bayesian classifier under a noise-exclusion model

arXiv:2601.07976v1 Announce Type: cross 
Abstract: This study advances task-based image quality assessment by developing an anthropomorphic thresholded visual-search model observer. The model is an ideal observer for thresholded data inspired by the human visual system, allowing selective processing of high-salience features to improve discrimination performance. By filtering out irrelevant variability, the model enhances diagnostic accuracy and computational efficiency.
  The observer employs a two-stage framework: candidate selection and decision-making. Using thresholded data during candidate selection refines regions of interest, while stage-specific feature processing optimizes performance. Simulations were conducted to evaluate the effects of thresholding on feature maps, candidate localization, and multi-feature scenarios. Results demonstrate that thresholding improves observer performance by excluding low-salience features, particularly in noisy environments. Intermediate thresholds often outperform no thresholding, indicating that retaining only relevant features is more effective than keeping all features.
  Additionally, the model demonstrates effective training with fewer images while maintaining alignment with human performance. These findings suggest that the proposed novel framework can predict human visual search performance in clinically realistic tasks and provide solutions for model observer training with limited resources. Our novel approach has applications in other areas where human visual search and detection tasks are modeled such as in computer vision, machine learning, defense and security image analysis.

قدمت دراسة حديثة نموذجًا للمراقب البصري الأنثروبومورفي المعتمد على العتبة، مما يعزز تقييم جودة الصورة القائم على المهام من خلال تقليد النظام البصري البشري. يقوم هذا النموذج بمعالجة الميزات ذات الأهمية العالية بشكل انتقائي، مما يحسن أداء التمييز والدقة التشخيصية بينما يقوم بتصفية التباين غير ذي الصلة.

Un estudio reciente ha presentado un modelo de observador visual antropomórfico umbralizado, mejorando la evaluación de la calidad de imagen basada en tareas al imitar el sistema visual humano. Este modelo procesa selectivamente características de alta saliencia, mejorando el rendimiento de discriminación y la precisión diagnóstica mientras filtra la variabilidad irrelevante.

Une étude récente a introduit un modèle d'observateur visuel anthropomorphique à seuil, améliorant l'évaluation de la qualité d'image basée sur des tâches en imitant le système visuel humain. Ce modèle traite sélectivement des caractéristiques de haute saillance, améliorant la performance de discrimination et la précision diagnostique tout en filtrant la variabilité non pertinente.

A recent study has introduced an anthropomorphic thresholded visual-search model observer, enhancing task-based image quality assessment by mimicking the human visual system. This model selectively processes high-salience features, improving discrimination performance and diagnostic accuracy while filtering out irrelevant variability.

Application of Ideal Observer for Thresholded Data in Search Task

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GauSSmart: Enhanced 3D Reconstruction through 2D Foundation Models and Geometric Filtering

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