提出像素级视觉信息价值评估方法，提升特征检测效率与精度
Learning Visual Information Utility with PIXER
- 基于贝叶斯泛化，单次计算像素的可靠性和重要性
- 在视觉里程计中使用该方法，轨迹误差降低31%，特征数量减少49%
- 适用于机器人、医疗影像等需高鲁棒性识别的场景
准确的特征检测是自动驾驶、三维重建、医学成像和遥感等计算机视觉任务的基础。尽管视觉特征的鲁棒性已有显著提升，但现有方法均无法在处理前量化特定特征算法所需的视觉信息价值。为此，本文提出PIXER与“Featureness”概念，反映视觉信息对鲁棒识别的固有兴趣与可靠性，不依赖特定特征类型。通过贝叶斯学习的泛化，该方法在单次过程中同时量化像素对视觉实用性的概率与不确定性，避免了蒙特卡洛采样等高开销操作，并支持自定义Featureness定义，适应多种应用。我们在视觉里程计中以Featureness选择性进行评估，平均实现RMSE轨迹降低31%，特征数量减少49%。
原文摘要 · Abstract (English)
Accurate feature detection is fundamental for various computer vision tasks, including autonomous robotics, 3D reconstruction, medical imaging, and remote sensing. Despite advancements in enhancing the robustness of visual features, no existing method measures the utility of visual information before processing by specific feature-type algorithms. To address this gap, we introduce PIXER and the concept of "Featureness," which reflects the inherent interest and reliability of visual information for robust recognition, independent of any specific feature type. Leveraging a generalization on Bayesian learning, our approach quantifies both the probability and uncertainty of a pixel's contribution to robust visual utility in a single-shot process, avoiding costly operations such as Monte Carlo sampling and permitting customizable featureness definitions adaptable to a wide range of applications. We evaluate PIXER on visual odometry with featureness selectivity, achieving an average of 31% improvement in RMSE trajectory with 49% fewer features.
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