Afzal Maken Fahira, Muthu Sundaram, Nguyen Chuong, Sun Changming, Tong Jinguang, Wang Shan, Tsuchida Russell, Howard David, Dunstall Simon, Petersson Lars
Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, Australia.
School of Computing, Australian National University (ANU), Canberra, ACT 2601, Australia.
Sensors (Basel). 2025 Feb 5;25(3):950. doi: 10.3390/s25030950.
High-resolution RGB-D sensors are widely used in computer vision, manufacturing, and robotics. The depth maps from these sensors have inherently high measurement uncertainty that includes both systematic and non-systematic noise. These noisy depth estimates degrade the quality of scans, resulting in less accurate 3D reconstruction, making them unsuitable for some high-precision applications. In this paper, we focus on quantifying the uncertainty in the depth maps of high-resolution RGB-D sensors for the purpose of improving 3D reconstruction accuracy. To this end, we estimate the noise model for a recent high-precision RGB-D structured light sensor called Zivid when mounted on a robot arm. Our proposed noise model takes into account the measurement distance and angle between the sensor and the measured surface. We additionally analyze the effect of background light, exposure time, and the number of captures on the quality of the depth maps obtained. Our noise model seamlessly integrates with well-known classical and modern neural rendering-based algorithms, from KinectFusion to Point-SLAM methods using bilinear interpolation as well as 3D analytical functions. We collect a high-resolution RGB-D dataset and apply our noise model to improve tracking and produce higher-resolution 3D models.
高分辨率RGB-D传感器广泛应用于计算机视觉、制造业和机器人技术领域。这些传感器生成的深度图固有地存在较高的测量不确定性，其中包括系统噪声和非系统噪声。这些有噪声的深度估计会降低扫描质量，导致三维重建的精度降低，使其不适用于某些高精度应用。在本文中，我们专注于量化高分辨率RGB-D传感器深度图中的不确定性，以提高三维重建精度。为此，我们估计了一种名为Zivid的最新高精度RGB-D结构光传感器安装在机器人手臂上时的噪声模型。我们提出的噪声模型考虑了传感器与被测表面之间的测量距离和角度。此外，我们还分析了背景光、曝光时间和采集次数对所得深度图质量的影响。我们的噪声模型与著名的经典和现代基于神经渲染的算法无缝集成，从KinectFusion到使用双线性插值以及三维解析函数的Point-SLAM方法。我们收集了一个高分辨率RGB-D数据集，并应用我们的噪声模型来改进跟踪并生成更高分辨率的三维模型。
