AmodalAppleSize_RGB-D

The AmodalAppleSize_RGB-D dataset comprises a collection of RGB-D apple tree images that can be used to train and test computer vision-based fruit detection and sizing methods. This dataset encompasses two distinct sets of data obtained from a Fuji and an Elstar apple orchards. The Fuji apple orchard sub-set consists of 3925 RGB-D images containing a total of 15335 apples annotated with both modal and amodal apple segmentation masks. Modal masks denote the visible portions of the apples, whereas amodal masks encompass both visible and occluded apple regions. Notably, this dataset is the first public resource to incorporate fruit amodal masks. This pioneering inclusion addresses a critical gap in existing datasets, enabling the development of robust automatic fruit sizing methods and accurate fruit visibility estimation, particularly in the presence of partial occlusions. Besides the fruit segmentation masks, the dataset also includes the fruit size (calliper) ground truth for each annotated apple. The second sub-set comprises 2731 RGB-D images capturing five Elstar apple trees at four distinct growth stages. This sub-set includes mean diameter information for each tree at every growth stage and serves as a valuable resource for evaluating fruit sizing methods trained with the first sub-set. The present data was employed in the research paper titled "Looking behind occlusions: a study on amodal segmentation for robust on-tree apple fruit size estimation".

AmodalAppleSize_RGB-D 数据集收录了多组RGB-D（红-绿-深度）苹果树图像，可用于训练和测试基于计算机视觉的水果检测与尺寸估算方法。该数据集包含两个独立的数据子集，分别采集自富士苹果园与埃尔斯特（Elstar）苹果园。富士苹果园子集包含3925张RGB-D图像，共标注了15335颗苹果，同时带有模态掩码（modal mask）与非模态掩码（amodal mask）。模态掩码指代苹果的可见区域，而非模态掩码则涵盖苹果的可见与被遮挡区域。值得注意的是，该数据集是首个公开的包含水果非模态掩码的数据集资源。这一开创性的收录填补了现有数据集的关键空白，可助力开发鲁棒的全自动水果尺寸估算方法与精准的水果可见性评估模型，尤其适用于存在部分遮挡的场景。除水果分割掩码外，该数据集还为每颗标注苹果提供了果实尺寸（卡钳测量值，caliper）的真实标注（ground truth）。第二个子集包含2731张RGB-D图像，采集了5棵埃尔斯特苹果树在4个不同生长阶段的场景。该子集包含每棵树在各生长阶段的平均直径信息，可作为评估使用第一子集训练得到的水果尺寸估算方法的优质资源。本数据集已被应用于题为《遮挡背后：面向鲁棒的树生苹果尺寸估算的非模态分割研究》的学术论文中。

搜集汇总

背景与挑战

背景概述

AmodalAppleSize_RGB-D数据集包含富士和Elstar苹果园的RGB-D图像，提供模态和amodal分割掩码及苹果大小真实值，首次公开水果amodal掩码，支持遮挡情况下的水果大小估计和可见性评估研究。