In recent years, depth sensors have become more and more affordable and have found their way into a growing amount of robotic systems. However, mono- or multi-modal sensor registration, often a necessary step for further processing, faces many challenges on raw depth images or point clouds. This paper presents a method of converting depth data into images capable of visualizing spatial details that are basically hidden in traditional depth images. After noise removal, a neighborhood of points forms two normal vectors whose difference is encoded into this new conversion. Compared to Bearing Angle images, our method yields brighter, higher-contrast images with more visible contours and more details. We tested feature-based pose estimation of both conversions in a visual odometry task and RGB-D SLAM. For all tested features, AKAZE, ORB, SIFT, and SURF, our new Flexion images yield better results than Bearing Angle images and show great potential to bridge the gap between depth data and classical computer vision. Source code is available here: https://rlsch.github.io/depth-flexion-conversion.

翻译：近年来，深度传感器成本逐渐降低，并越来越多地应用于机器人系统。然而，单模态或多模态传感器配准作为后续处理的必要步骤，在处理原始深度图像或点云时面临诸多挑战。本文提出一种将深度数据转换为能够可视化传统深度图像中隐藏空间细节的图像方法。经过去噪处理后，通过点邻域形成两个法向量，将两者的差异编码至新转换图像中。与方位角图像相比，本方法生成的图像更明亮、对比度更高，轮廓更清晰且细节更丰富。我们在视觉里程计任务和RGB-D SLAM中测试了两种转换方法的基于特征位姿估计。对于所有测试特征（AKAZE、ORB、SIFT和SURF），本文提出的Flexion图像均优于方位角图像，展现出弥合深度数据与经典计算机视觉之间差距的巨大潜力。源代码获取地址：https://rlsch.github.io/depth-flexion-conversion