arXiv:2605.31534v1 Announce Type: new 
Abstract: Three-dimensional scene reconstruction depends on local image evidence that is both visually discriminative and geometrically useful. Fixed feature thresholds and uniform feature budgets are easy to deploy, but they can waste computation on repeated texture, low-parallax regions, or unstable points. This paper proposes an adaptive feature-optimized vision front end for 3D reconstruction. The method scores candidate features by texture, repeatability, distinctiveness, expected triangulation angle, and spatial coverage, then allocates a per-view feature budget to maximize useful tracks under a fixed reconstruction pipeline. A small synthetic multi-view prototype evaluates four selection policies across corridor, facade, object-table, and cluttered scenes. Compared with random, texture-only, and uniform-grid baselines, the adaptive policy obtains the best quality-aware completeness and the lowest aggregate reconstruction RMSE while preserving broad image coverage. The result is not a replacement for modern learned matching or neural reconstruction systems; it is a modular front-end policy that can make classical and learned 3D pipelines more deliberate about which visual evidence they spend compute on.

تقدم دراسة جديدة واجهة رؤية محسّنة بالميزات التكيفية لإعادة بناء المشاهد ثلاثية الأبعاد، مما يعزز العملية من خلال تقييم الميزات المرشحة بناءً على معايير متنوعة مثل الملمس والتميز. تهدف هذه الطريقة إلى زيادة المسارات المفيدة مع تقليل الهدر الحاسوبي في مهام إعادة البناء ثلاثية الأبعاد.

Un nuevo estudio presenta un frente de visión optimizado por características adaptativas para la reconstrucción de escenas 3D, mejorando el proceso al puntuar características candidatas según varios criterios como la textura y la distintividad. Este método busca maximizar las pistas útiles mientras minimiza el desperdicio computacional en tareas de reconstrucción 3D.

Une nouvelle étude présente un front-end de vision optimisé par caractéristiques adaptatives pour la reconstruction de scènes 3D, améliorant le processus en notant les caractéristiques candidates selon divers critères tels que la texture et la distinctivité. Cette méthode vise à maximiser les pistes utiles tout en minimisant le gaspillage computationnel dans les tâches de reconstruction 3D.

A new study presents an adaptive feature-optimized vision front end for 3D scene reconstruction, enhancing the process by scoring candidate features based on various criteria such as texture and distinctiveness. This method aims to maximize useful tracks while minimizing computational waste in 3D reconstruction tasks.