Feature-Optimized Vision for Adaptive 3D Scene Reconstruction
This work provides an incremental improvement for classical and learned 3D reconstruction pipelines by making feature selection more deliberate and efficient, potentially benefiting researchers and practitioners working on 3D scene understanding.
This paper addresses the problem of efficiently selecting visual features for 3D scene reconstruction by proposing an adaptive feature-optimized vision front end. The method scores candidate features based on multiple criteria and allocates a per-view budget to maximize useful tracks, resulting in the best quality-aware completeness and lowest aggregate reconstruction RMSE compared to baselines in synthetic scenes.
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.