论文标题
Buff：用于光约束3D重建的突发功能查找器
BuFF: Burst Feature Finder for Light-Constrained 3D Reconstruction
论文作者
论文摘要
夜间使用常规视觉摄像机运行的机器人由于噪声有限的图像而在重建中面临重大挑战。先前的工作表明，爆发成像技术可用于部分克服这一问题。在本文中，我们开发了一种新型的功能检测器，该特征检测器直接在图像爆发上运行，从而在极低的光线条件下增强了基于视觉的重建。我们的方法通过在多尺度和多运动空间中共同搜索来找到每个爆发中定义明确的尺度和明显运动的关键点。因为我们在图像具有较高的信噪比的阶段描述了这些特征，所以检测到的特征比传统噪声图像和突发的图像和爆发图像的最新图像更准确，并且表现出很高的精度，回忆和匹配性能。我们展示了提高的功能性能和摄像头姿势估计，并使用我们的功能探测器在挑战性的光线约束场景中展示了改进的结构，从而改善了结构。我们的功能Finder为在弱光方案和应用程序（包括夜间操作）中运行的机器人提供了重要的一步。
Robots operating at night using conventional vision cameras face significant challenges in reconstruction due to noise-limited images. Previous work has demonstrated that burst-imaging techniques can be used to partially overcome this issue. In this paper, we develop a novel feature detector that operates directly on image bursts that enhances vision-based reconstruction under extremely low-light conditions. Our approach finds keypoints with well-defined scale and apparent motion within each burst by jointly searching in a multi-scale and multi-motion space. Because we describe these features at a stage where the images have higher signal-to-noise ratio, the detected features are more accurate than the state-of-the-art on conventional noisy images and burst-merged images and exhibit high precision, recall, and matching performance. We show improved feature performance and camera pose estimates and demonstrate improved structure-from-motion performance using our feature detector in challenging light-constrained scenes. Our feature finder provides a significant step towards robots operating in low-light scenarios and applications including night-time operations.
