基于CGRSNet的残缺油桃外形点云补全方法

A point cloud completion method based on CGRSNet for incomplete nectarine shape

[目的]利用深度相机采集到的油桃数据在进行三维重建时,存在摄像头视野限制,导致油桃点云出现缺失的现象,严重影响油桃表型分析的准确度。为了提高油桃分级分类的准确率和工作效率,提出一种基于三维重建的油桃片外形参数估测方法。[方法]提出了一种基于真实结构的粗粒度点云生成网络(coarse-grained realistic structure with point cloud generation network,CGRSNet)。该网络首先通过编码、解码机制构建点云补全网络,通过新的采样算法对原始点云提取不同维度下的特征信息,提升油桃表型的特征提取能力,然后自解码器自上而下进行多维度点云补全,最终得到完整的油桃形状,最后用模型的参数预估模块预估油桃体积。利用多组参数构建的油桃几何模型离散成点云数据输入CGRSNet网络,得到油桃几何模型外形补全的预训练模型,再利用真实油桃点云数据对预训练模型CGRSNet做模型迁移的参数微调。[结果]该补全网络对油桃数据集补全结果的倒角距离为0.196 cm。经线性回归分析,得出体积估测的RMSE和R^(2)分数为2.47 cm^(3)和0.94,相较于PF-Net的RMSE和R^(2)分数提升约0.88 cm^(3)和0.01。[结论]本文提出的基于CGRSNet的油桃外形参数估测算法具有较好的实用性和精确度。

[Objectives]Because of the limit of camera field,the walnut point cloud suffers from missing points,which detrimentally affects the accuracy of walnut phenotype analysis.To enhance the accuracy and expedite the efficiency of walnut grading and classification,this paper proposed a walnut external parameter estimation approach with three-dimensional reconstruction.[Methods]We introduced a coarse-grained realistic structure with the point cloud generation network(CGRSNet)to form a point cloud completion network with an encoding-decoding mechanism.Firstly,a new sampling algorithm was implemented to extract feature information of the original point cloud in different dimensions and thereby increased the feature extraction effectiveness of walnut phenotype.Afterward,the autoencoder was utilized to reconstruct the point cloud in multiple dimensions,and a complete walnut shape was eventually obtained.In the end,the parameters of the model were applied to estimate the volume of walnut.The walnut geometry model generated by multiple parameters was digitized into the point cloud data to feed CGRSNet and get a pre-trained walnut geometry model through shape completion.A fine-tuning process was then conducted with the real walnut point cloud data.[Results]The chamfer distance of the completion network for walnut data set was 0.196 cm.According to the linear regression analysis,the RMSE and R^(2) score of the volume estimation were 2.47cm and 0.94,respectively,which were 0.88 cm and 0.01 higher than those of PF-Net,respectively.[Conclusions]The walnut external parameter estimation algorithm based on CGRSNet proposed in this paper presents satisfactory practicability and accuracy.