基于SLAM与神经辐射场的柑橘幼苗三维重建方法

    3D reconstruction of citrus seedlings based on SLAM and NeRF

    • 摘要:
      目的 针对现有三维重建技术难以获取柑橘幼苗精细三维点云及其三维表型参数来表征幼苗状态的问题,提出一种基于同时定位与地图构建算法(Simultaneous localization and mapping,SLAM)与神经辐射场(Neural radiance fields,NeRF)的柑橘幼苗三维重建方法。
      方法 以1年期的柑橘幼苗为研究对象,首先,利用深度传感器获取柑橘幼苗的RGB和深度图;其次,使用SLAM计算深度传感器在每一帧图像的位姿;然后训练幼苗NeRF网络,将附有位姿的多视角图像输入多层感知机(Multilayer erceptron, MLP);最后,通过体积渲染监督训练,重建高精细的三维实景点云模型。
      结果 本文方法重建的柑橘幼苗三维模型,在色彩与纹理方面极具真实性,模型轮廓清晰、层次分明,具有实景级的精度。该模型可有效提取柑橘幼苗的三维表型参数,株高、宽幅长、宽幅宽和胸径的准确率分别为97.94%、 93.95%、94.11%和97.62%。
      结论 研究有助于加快优良柑橘幼苗的选育进程,可为柑橘产业的可持续性发展提供技术支持。

       

      Abstract:
      Objective Aiming at the problem that it is difficult to obtain the accurate 3D point cloud of citrus seedlings and their 3D phenotypic parameters to characterize the state of seedlings with the existing 3D reconstruction techniques, this paper proposes a method based on the simultaneous localization and mapping (SLAM) and neural radiance fields (NeRF) for 3D reconstruction of citrus seedlings.
      Method One-year old citrus seedlings were taken as the research object. Firstly, a depth sensor was used to capture the RGB map and depth map of the citrus seedling. Secondly, SLAM was employed to obtain the poses of the depth sensor in each frame of the image. Then, NeRF was trained for citrus seedlings, and the multi-view images with attached positional pose were fed into the multilayer erceptron (MLP). Finally, through supervised training with volume rendering, a high-precision 3D realistic point cloud model of citrus seedlings was reconstructed.
      Result The 3D model of citrus seedlings reconstructed by this method was highly realistic in terms of color and texture, with clear contours and distinct layers, and had real-world level accuracy. Based on this model, the 3D phenotypic parameters of citrus seedlings could be effectively extracted with the accuracy of 97.94% for plant height, 93.95% for breadth length, 94.11% for breadth width and 97.62% for stem thickness.
      Conclusion This study helps to accelerate the selection and nursery process of excellent citrus seedlings and provides a technical support for the sustainable development of the citrus industry.

       

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