利用彩色图像修正深度相机中的漂浮像素，提升测距准确性
Color-Guided Flying Pixel Correction in Depth Images
- 通过RGB与深度图对齐，构建3D邻域模拟ToF成像过程
- 两步迭代算法：先识别漂浮像素，再通过最小二乘优化求解真实深度
- 适用于带彩色相机的深度摄像头，尤其在遮挡场景下表现优异
本文提出一种新方法，用于校正基于飞行时间（ToF）传感器捕获数据中的漂浮像素（FP）伪影。当前景和背景物体信号同时到达同一传感器像素时，会产生看似可信却错误的深度估计，表现为物体间的“漂浮”现象。商用RGB-D相机配备互补的ToF传感器与RGB相机，可同步获取深度与彩色图像。本方法利用两者对齐特性，估计FP的真实深度值。通过在每个点周围定义一个3D邻域，模拟ToF相机的视场范围；采用两步迭代校正算法，首先识别漂浮像素，随后通过求解最小二乘优化问题估计其真实深度。实验结果表明，该方法在深度估计精度上达到文献中其他算法的水平。
原文摘要 · Abstract (English)
We present a novel method to correct flying pixels within data captured by Time-of-flight (ToF) sensors. Flying pixel (FP) artifacts occur when signals from foreground and background objects reach the same sensor pixel, leading to a confident yet incorrect depth estimation in space - floating between two objects. Commercial RGB-D cameras have a complementary setup consisting of ToF sensors to capture depth in addition to RGB cameras. We propose a novel method to correct FPs by leveraging the aligned RGB and depth image in such RGB-D cameras to estimate the true depth values of FPs. Our method defines a 3D neighborhood around each point, representing a "field of view" that mirrors the acquisition process of ToF cameras. We propose a two-step iterative correction algorithm in which the FPs are first identified. Then, we estimate the true depth value of FPs by solving a least-squares optimization problem. Experimental results show that our proposed algorithm estimates the depth value of FPs as accurately as other algorithms in the literature.
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