深度模组A常规方法杂散光校正测试数据

数据用于某一场景下的深度模组A常规方法杂散光校正测试、定量分析该场景下的校正效果，及指导校正算法完善。采集一定距离d前景遮挡，一定距离D后景背景下的原始深度数据——原始数据tof-x、原始数据tof-y、原始数据tof-z；采集有无前景遮挡的数据各一组，并根据预定义的杂散光模型在频域对模型参数进行优化求解，使得校正后数据与无前景遮挡数据差值最小，以获得模型杂散光标定参数。将该距离d前景遮挡，距离D后景背景下的原始深度数据与所得杂散光模型进行卷积，得到校正后的深度数据——校正后数据tof-x、校正后数据tof-y、校正后数据tof-z。This dataset is designed for the stray light correction test using conventional methods for depth module A in a specific scenario, aiming to quantitatively analyze the correction effect in this scenario and guide the optimization of correction algorithms. First, raw depth data under the scenario with foreground occlusion at distance d and background at distance D is collected, including raw datasets: tof-x, tof-y, and tof-z. Two sets of data are collected respectively with and without foreground occlusion. Then, model parameters are optimized and solved in the frequency domain based on a pre-defined stray light model, to minimize the difference between the corrected data and the data without foreground occlusion, thus obtaining the stray light calibration parameters of the model. Finally, the raw depth data collected under the scenario of foreground occlusion at distance d and background at distance D is convolved with the obtained stray light model, to generate the corrected depth data: corrected data tof-x, corrected data tof-y, and corrected data tof-z.

搜集汇总

数据集介绍

特点

该数据集包含深度模组A的杂散光校正测试数据，涵盖安装参数、前后景信息和原始及校正后的tof数据，用于校正效果分析和算法优化。