深度模组A自适应算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自适应算法杂散光校正测试、定量分析该场景下该校正算法的校正效果，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；采集不同前景距离遮挡的数据若干组，并分别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，获得杂散光标定参数集，同时结合前景距离信息，实现距离自适应的杂散光模型。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与所得自适应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 intended for stray light correction testing of the adaptive algorithm for depth module A in a specific scenario, quantitative evaluation of the correction performance of this algorithm under this scenario, and providing guidance for the optimization of the correction algorithm. First, collect raw depth data under the condition of foreground occlusion at a specified distance d and background at a specified distance D: raw data tof-x, raw data tof-y, and raw data tof-z. Then, collect multiple sets of data with foreground occlusion at varying distances, and for each set, optimize and solve the model parameters in the frequency domain using the predefined stray light model, so as to minimize the difference between the corrected data and the data collected without foreground occlusion, thereby obtaining the stray light calibration parameter set. Additionally, by integrating the foreground distance information, a distance-adaptive stray light model is developed. Finally, convolve the raw depth data collected under the condition of foreground occlusion at a fixed distance d and background at a fixed distance D with the obtained adaptive stray light model to yield 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1122条记录，主要用于深度模组A自适应算法杂散光校正测试，涉及模组安装参数、采集地点及前景后景信息，旨在定量分析校正算法效果并指导算法优化。