基于边缘强化神经拉动模型的三维表面重建

3D surface reconstruction method through neural-pull based on edge enhancement

通过学习空间点云数据的符号距离函数(Signed Distance Functions, SDFs)进行三维表面重建是当前的研究热点。为重建出高精度的水密模型,神经拉动(Neural-pull, NP)采用点云拉动,以训练同步更新SDFs,但在实际重建过程中,重建模型会因为点云存在噪声和缺失而导致重建结果不够精细,带来错误的表面重建。针对以上问题,引入边缘提取强化输入点云的边缘信息,提出利用残差学习机制的边缘强化神经拉动模型(Neural Pull based on Edge Enhancement, NPEE)。为确保重建表面平滑的同时能够获取更多的表面细节,该方法在保留原本神经网络用于学习SDFs的基础上引入一个新的网络,利用残差学习机制学习点云的边缘SDFs。同时在原始点云的基础上,引入边缘因子σ,结合学习的边缘SDFs,通过点云边缘的鲁棒提取强化输入点云。为验证算法模型的优化效果,采用目前广泛使用的ABC数据集、斯坦福扫描模型和模拟扫描数据集设计对比实验,实验结果以及评估指标(CD)表明,NPEE可以有效改善神经拉动算法在边缘表面重建的缺陷,同时和其他重建方法相比,NPEE在面对稀疏点云和含噪点云时仍能保证重建的精确性和完整性。

Objective Surface reconstruction refers to the process of reconstructing a continuous 3D surface from discrete spatial point clouds data.Learning signed distance functions(SDFs)of an object through spatial discrete point clouds to reconstruct the implicit surface of an object is currently the main research strategy.Neural-pull(NP)is a method to obtain high-quality SDFs from discrete point clouds information to reconstruct original surface.The key lies in using the predicted SDFs and the gradient information of the query point to pull the query point onto the reconstructed surface(Fig.1).The advantage of this method is that the model training process does not require ground truth(GT)SDFs as supervision,and at the same time,the signed distance value and gradient information are updated simultaneously during the training process.However,due to the inevitable noise and defects in the actually obtained point cloud data,NP will cause edge defects on the reconstructed surface during reconstruction.Therefore,in order to improve the reconstruction effect of the algorithm,solve the edge defects problem occurs in NP's sparse point clouds reconstruction,this paper proposes an improvement strategy,called neural-pull based on edge enhancement(NPEE).Methods In order to solve the NP edge overfitting problem,this paper proposes the NPEE framework(Fig.2).It consists of edge-construction network(EN)and NP network.EN(Fig.3)uses a residual learning mechanism to learn the edge SDFs of point clouds,ensuring sufficient edge accuracy while ensuring training efficiency.At the same time,based on the original point cloud,the edge factor is introduced,combined with the learned edge SDFs,to enhance the input point cloud through robust point cloud edge extraction(Fig.4).NPEE still retains the original neural network(Fig.5)for learning SDFs,so that the network that is prone to overfitting can obtain more surface details while ensuring the smoothness of the reconstructed surface.Results and Discussions Using the ABC data set,Stanford scanning model and deep geometric prior data as the ground truth,ideal point clouds reconstruction experiments(Fig.7),sparse point clouds reconstruction experiments(Fig.8)and noisy points cloud reconstruction experiments(Fig.12)are designed to evaluate the algorithm capabilities.The reconstruction results of DeepSDF,Onsurface and NP are compared,and the chamfer distance(CD)of the three groups of experiments are calculated respectively.The experimental results are as follows(Tab.1-6).The reconstruction results of NPEE can show the complete reconstruction model and sufficient edge details.Conclusions In order to improve the reconstruction effect of the algorithm,the edge defects problem occuring in NP's sparse point clouds reconstruction is solved,an improvement strategy is designed,called NPEE.To ensure the reconstructed surface is smooth while obtaining more surface details,on the basis of retaining the original neural network,a new network which uses the residual learning mechanism is designed for learning SDFs of the point cloud while ensuring training efficiency and getting sufficient edge accuracy.At the same time,on the basis of the original point cloud,the edge factor is introduced,to enhance the input point cloud through robust extraction of point cloud edges.Three sets of comparative experiments were designed using widely used ground truth data sets,namely ideal point cloud reconstruction experiment,sparse point cloud reconstruction experiment and noisy point cloud reconstruction experiment.Experimental results and evaluation index CD show that NPEE can effectively improve the defects of the NP algorithm in edge surface reconstruction,showing superiority compared with other algorithms.