1.软件版本
matlab2021a
2.系统概述
这里，采用的pointnet网络结构如下图所示：
在整体网络结构中，
首先进行set abstraction，这一部分主要即对点云中的点进行局部划分，提取整体特征，如图可见，在set abstraction中，主要有Sampling layer、Grouping layer、以及PointNet layer三层构成，sampling layer即完成提取中心点工作，采用fps算法，而在grouping中，即完成group操作，采用mrg或msg方法，最后对于提取出得点，使用pointnet进行特征提取。在msg中，第一层set abstraction取中心点512个，半径分别为0.1、0.2、0.4，每个圈内的最大点数为16，32，128。
Sampling layer
采样层在输入点云中选择一系列点，由此定义出局部区域的中心。采样算法使用迭代最远点采样方法 iterative farthest point sampling（FPS）。先随机选择一个点，然后再选择离这个点最远的点作为起点，再继续迭代，直到选出需要的个数为止相比随机采样，能更完整得通过区域中心点采样到全局点云
Grouping layer
目的是要构建局部区域，进而提取特征。思想就是利用临近点，并且论文中使用的是neighborhood ball，而不是KNN，是因为可以保证有一个fixed region scale，主要的指标还是距离distance。
Pointnet layer
在如何对点云进行局部特征提取的问题上，利用原有的Pointnet就可以很好的提取点云的特征，由此在Pointnet++中，原先的Pointnet网络就成为了Pointnet++网络中的子网络，层级迭代提取特征。
3.部分核心程序
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clc;
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clear;
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close all;
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warning off;
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addpath(genpath(pwd));
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rng(
'default')
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%****************************************************************************
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%更多关于matlab和fpga的搜索“fpga和matlab”的CSDN博客:
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%matlab/FPGA项目开发合作
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%https://blog.csdn.net/ccsss22?type=blog
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%****************************************************************************
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dsTrain = PtCloudClassificationDatastore(
'train');
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dsVal = PtCloudClassificationDatastore(
'test');
-
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ptCloud = pcread(
'Chair.ply');
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label =
'Chair';
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figure;pcshow(ptCloud)
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xlabel(
"X");ylabel(
"Y");zlabel(
"Z");title(label)
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dsLabelCounts = transform(dsTrain,@(data){data{
2} data{
1}.Count});
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labelCounts = readall(dsLabelCounts);
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labels = vertcat(labelCounts{:,
1});
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counts = vertcat(labelCounts{:,
2});
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figure;histogram(labels);title(
'class distribution')
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-
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rng(
0)
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[G,classes] = findgroups(labels);
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numObservations = splitapply(@numel,labels,G);
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desiredNumObservationsPerClass = max(numObservations);
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filesOverSample=[];
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for i=
1:numel(classes)
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if i==
1
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targetFiles = {dsTrain.Files{
1:numObservations(i)}};
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else
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targetFiles = {dsTrain.Files{numObservations(i-
1)+
1:sum(numObservations(
1:i))}};
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end
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% Randomly replicate the point clouds belonging to the infrequent classes
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files = randReplicateFiles(targetFiles,desiredNumObservationsPerClass);
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filesOverSample = vertcat(filesOverSample,files');
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end
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dsTrain.Files=filesOverSample;
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-
-
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dsTrain.Files = dsTrain.Files(randperm(length(dsTrain.Files)));
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-
-
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dsTrain.MiniBatchSize =
32;
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dsVal.MiniBatchSize = dsTrain.MiniBatchSize;
-
-
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dsTrain = transform(dsTrain,@augmentPointCloud);
-
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data = preview(dsTrain);
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ptCloud = data{
1,
1};
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label = data{
1,
2};
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figure;pcshow(ptCloud.Location,[
0
0
1],
"MarkerSize",
40,
"VerticalAxisDir",
"down")
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xlabel(
"X");ylabel(
"Y");zlabel(
"Z");title(label)
-
-
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minPointCount = splitapply(@min,counts,G);
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maxPointCount = splitapply(@max,counts,G);
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meanPointCount = splitapply(@(x)round(mean(x)),counts,G);
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stats = table(classes,numObservations,minPointCount,maxPointCount,meanPointCount)
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numPoints =
1000;
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dsTrain = transform(dsTrain,@(data)selectPoints(data,numPoints));
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dsVal = transform(dsVal,@(data)selectPoints(data,numPoints));
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dsTrain = transform(dsTrain,@preprocessPointCloud);
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dsVal = transform(dsVal,@preprocessPointCloud);
-
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data = preview(dsTrain);
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figure;pcshow(data{
1,
1},[
0
0
1],
"MarkerSize",
40,
"VerticalAxisDir",
"down");
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xlabel(
"X");ylabel(
"Y");zlabel(
"Z");title(data{
1,
2})
-
-
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inputChannelSize =
3;
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hiddenChannelSize1 = [
64,
128];
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hiddenChannelSize2 =
256;
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[parameters.InputTransform, state.InputTransform] = initializeTransform(inputChannelSize,hiddenChannelSize1,hiddenChannelSize2);
-
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inputChannelSize =
3;
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hiddenChannelSize = [
64
64];
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[parameters.SharedMLP1,state.SharedMLP1] = initializeSharedMLP(inputChannelSize,hiddenChannelSize);
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inputChannelSize =
64;
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hiddenChannelSize1 = [
64,
128];
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hiddenChannelSize2 =
256;
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[parameters.FeatureTransform, state.FeatureTransform] = initializeTransform(inputChannelSize,hiddenChannelSize,hiddenChannelSize2);
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inputChannelSize =
64;
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hiddenChannelSize =
64;
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[parameters.SharedMLP2,state.SharedMLP2] = initializeSharedMLP(inputChannelSize,hiddenChannelSize);
-
-
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inputChannelSize =
64;
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hiddenChannelSize = [
512,
256];
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numClasses = numel(classes);
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[parameters.ClassificationMLP, state.ClassificationMLP] = initializeClassificationMLP(inputChannelSize,hiddenChannelSize,numClasses);
-
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numEpochs =
60;
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learnRate =
0.001;
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l2Regularization =
0.1;
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learnRateDropPeriod =
15;
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learnRateDropFactor =
0.5;
-
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gradientDecayFactor =
0.9;
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squaredGradientDecayFactor =
0.999;
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avgGradients = [];
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avgSquaredGradients = [];
-
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[lossPlotter, trainAccPlotter,valAccPlotter] = initializeTrainingProgressPlot;
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% Number of classes
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numClasses = numel(classes);
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% Initialize the iterations
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iteration =
0;
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% To calculate the time for training
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start = tic;
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% Loop over the epochs
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for epoch =
1:numEpochs
-
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% Reset training and validation datastores.
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reset(dsTrain);
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reset(dsVal);
-
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% Iterate through data set.
-
while hasdata(dsTrain)
% if no data to read, exit the loop to start the next epoch
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iteration = iteration +
1;
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% Read data.
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data = read(dsTrain);
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% Create batch.
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[XTrain,YTrain] = batchData(data,classes);
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% Evaluate the model gradients and loss using dlfeval and the
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% modelGradients function.
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[gradients, loss, state, acc] = dlfeval(@modelGradients,XTrain,YTrain,parameters,state);
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% L2 regularization.
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gradients = dlupdate(@(g,p) g + l2Regularization*p,gradients,parameters);
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% Update the network parameters using the Adam optimizer.
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[parameters, avgGradients, avgSquaredGradients] = adamupdate(parameters, gradients, ...
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avgGradients, avgSquaredGradients, iteration,learnRate,gradientDecayFactor, squaredGradientDecayFactor);
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% Update the training progress.
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D = duration(
0,
0,toc(start),
"Format",
"hh:mm:ss");
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title(lossPlotter.Parent,
"Epoch: " + epoch +
", Elapsed: " + string(D))
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addpoints(lossPlotter,iteration,double(gather(extractdata(loss))))
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addpoints(trainAccPlotter,iteration,acc);
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drawnow
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end
-
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% Create confusion matrix
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cmat = sparse(numClasses,numClasses);
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% Classify the validation data to monitor the tranining process
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while hasdata(dsVal)
-
data = read(dsVal);
% Get the next batch of data.
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[XVal,YVal] = batchData(data,classes);
% Create batch.
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% Compute label predictions.
-
isTrainingVal =
0;
%Set at zero for validation data
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YPred = pointnetClassifier(XVal,parameters,state,isTrainingVal);
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% Choose prediction with highest score as the class label for
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% XTest.
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[~,YValLabel] = max(YVal,[],
1);
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[~,YPredLabel] = max(YPred,[],
1);
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cmat = aggreateConfusionMetric(cmat,YValLabel,YPredLabel);
% Update the confusion matrix
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end
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% Update training progress plot with average classification accuracy.
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acc = sum(diag(cmat))./sum(cmat,
"all");
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addpoints(valAccPlotter,iteration,acc);
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% Update the learning rate
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if mod(epoch,learnRateDropPeriod) ==
0
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learnRate = learnRate * learnRateDropFactor;
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end
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reset(dsTrain);
% Reset the training data since all the training data were already read
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% Shuffle the data at every epoch
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dsTrain.UnderlyingDatastore.Files = dsTrain.UnderlyingDatastore.Files(randperm(length(dsTrain.UnderlyingDatastore.Files)));
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reset(dsVal);
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end
-
-
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cmat = sparse(numClasses,numClasses);
% Prepare sparse-double variable to do like zeros(2,2)
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reset(dsVal);
% Reset the validation data
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data = readall(dsVal);
% Read all validation data
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[XVal,YVal] = batchData(data,classes);
% Create batch.
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% Classify the validation data using the helper function pointnetClassifier
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YPred = pointnetClassifier(XVal,parameters,state,isTrainingVal);
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% Choose prediction with highest score as the class label for
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% XTest.
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[~,YValLabel] = max(YVal,[],
1);
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[~,YPredLabel] = max(YPred,[],
1);
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% Collect confusion metrics.
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cmat = aggreateConfusionMetric(cmat,YValLabel,YPredLabel);
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figure;chart = confusionchart(cmat,classes);
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acc = sum(diag(cmat))./sum(cmat,
"all")
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4.仿真结论
5.参考文献
[1][1] Qi C R , Su H , Mo K , et al. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017.资源同名下载
转载：https://blog.csdn.net/ccsss22/article/details/125434324
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