FruitNeRF：一个基于神经辐射场的统一水果计数框架
FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework
AI总结：
FruitNeRF是一个基于神经辐射场的统一水果计数框架，通过单目图像分割和3D点云聚类实现精确计数，避免重复计数，适用于多种水果类型。
摘要：
我们介绍了FruitNeRF，这是一个统一的全新水果计数框架，它利用最先进的视图合成方法直接在3D中对任何水果类型进行计数。我们的框架接收由单目相机捕获的一组无序位姿图像，并在每张图像中分割水果。为了使我们的系统独立于水果类型，我们采用了一个基础模型，为任何水果生成二值分割掩膜。利用RGB和语义这两种模态，我们训练了一个语义神经辐射场。通过对隐式Fruit Field进行均匀体积采样，我们获得了仅包含水果的点云。通过对提取的点云应用级联聚类，我们的方法实现了精确的水果计数。使用神经辐射场相比对象跟踪或光流等传统方法提供了显著优势，因为计数本身被提升到了3D。我们的方法防止了水果的重复计数，并避免了计数无关的水果。我们使用真实世界和合成数据集评估了我们的方法。真实世界数据集包括三棵具有手动计数真值的苹果树，一个包含一行且具有真值水果位置的基准苹果数据集，而合成数据集包含多种水果类型，包括苹果、李子、柠檬、梨、桃子和芒果。此外，我们评估了使用基础模型进行水果计数与使用U-Net的性能对比。
英文摘要：
We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count.The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit.We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mango.Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
