Feature-Optimized Vision for Adaptive 3D Scene Reconstruction

2026-05-29

AI Summary

This work addresses the inefficiency of fixed feature thresholds and uniform feature budgets in traditional 3D reconstruction, which often waste computational resources in regions with repetitive textures or low parallax. The authors propose an adaptive front-end visual strategy that dynamically allocates per-view feature budgets through a multi-criteria scoring mechanism, incorporating texture richness, repeatability, distinctiveness, expected triangulation angle, and spatial coverage. This modular, quality-aware feature selection approach seamlessly integrates into both classical and learning-based reconstruction pipelines, maximizing the number of effectively tracked features within a fixed processing workflow. Experiments demonstrate that, compared to baseline strategies such as random sampling, texture-only selection, or uniform grid sampling, the proposed method significantly improves reconstruction completeness and accuracy—evidenced by lower RMSE—across diverse scenes while maintaining broad image coverage.

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.

Problem

• 3D scene reconstruction
• feature selection
• adaptive vision
• computational efficiency
• visual evidence

Innovation

• adaptive feature selection
• 3D scene reconstruction
• feature budget allocation
• triangulation-aware scoring
• modular vision front-end