用可重光照3D重建快速生成光照变化数据，提升视觉特征鲁棒性。
An Illumination-Robust Feature Extractor Augmented by Relightable 3D Reconstruction
- 利用可重光照3D重建技术生成多光照条件数据。
- 自监督框架提升关键点重复性和描述子相似性。
- 适合需要光照不变特征的机器人导航场景。
视觉特征依赖局部强度和梯度方向，在机器人导航与定位中应用广泛。然而，光照变化常干扰特征提取，影响实际应用。以往方法通过构建光照变化数据集缓解问题，但成本高、耗时长。本文提出一种光照鲁棒特征提取器设计流程，采用最近发展的可重光照3D重建技术，实现快速、直接生成不同光照条件下的数据。提出一种自监督框架，使特征在良好与恶劣光照条件下均具备良好的关键点重复性和描述子相似性。实验验证了该方法在鲁棒特征提取上的有效性，消融研究也表明自监督框架设计的有效性。
原文摘要 · Abstract (English)
Visual features, whose description often relies on the local intensity and gradient direction, have found wide applications in robot navigation and localization in recent years. However, the extraction of visual features is usually disturbed by the variation of illumination conditions, making it challenging for real-world applications. Previous works have addressed this issue by establishing datasets with variations in illumination conditions, but can be costly and time-consuming. This paper proposes a design procedure for an illumination-robust feature extractor, where the recently developed relightable 3D reconstruction techniques are adopted for rapid and direct data generation with varying illumination conditions. A self-supervised framework is proposed for extracting features with advantages in repeatability for key points and similarity for descriptors across good and bad illumination conditions. Experiments are conducted to demonstrate the effectiveness of the proposed method for robust feature extraction. Ablation studies also indicate the effectiveness of the self-supervised framework design.
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