根据视觉质量动态分配特征点，提升3D重建精度与效率
Feature-Optimized Vision for Adaptive 3D Scene Reconstruction
- 按纹理、重复性等五维度评分，自适应分配每帧特征点数量
- 在四类场景中实现最低重建均方误差，且完整度最优
- 可嵌入现有系统，让传统与学习型重建更智能地用算力
三维场景重建依赖于既具视觉区分性又具几何价值的局部图像特征。固定特征阈值和均匀特征预算虽易部署，却会浪费计算资源在重复纹理、低视差区域或不稳定点上。本文提出一种自适应特征优化视觉前端用于3D重建：通过纹理、重复性、独特性、预期三角化角和空间覆盖率五个维度对候选特征评分，并在固定重建流程下为每帧分配最优特征预算以最大化有效轨迹数。使用小型合成多视角原型，在走廊、立面、物体-桌面及杂乱场景中评估四种选择策略。相比随机、仅纹理和均匀网格基线，自适应策略在质量感知完整度上表现最佳，且总重建均方误差最低，同时保持广泛图像覆盖。该方法并非替代现代学习匹配或神经重建系统，而是一个模块化前端策略，使经典与学习型3D流水线能更精准地决定计算资源投入的视觉证据。
原文摘要 · Abstract (English)
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.
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