对以上代码进行详细注释以及列出所需的库

以下是MATLAB PointNet点云分类代码的详细注释：

% 加载数据
load('modelnet10_batch_1.mat'); % 加载数据文件

% 数据预处理
trainPoints = trainData(:, :, :, 1:800); % 取出训练数据点云
trainLabels = trainLabels(1:800); % 取出训练数据标签
testPoints = trainData(:, :, :, 801:end); % 取出测试数据点云
testLabels = trainLabels(801:end); % 取出测试数据标签

trainPoints = permute(trainPoints, [2 1 3]); % 调整数据维度
trainPoints = reshape(trainPoints, [size(trainPoints, 1) size(trainPoints, 2) * size(trainPoints, 3)]); % 将点云数据转换为张量
testPoints = permute(testPoints, [2 1 3]); % 调整数据维度
testPoints = reshape(testPoints, [size(testPoints, 1) size(testPoints, 2) * size(testPoints, 3)]); % 将点云数据转换为张量

trainPoints = trainPoints'; % 转置张量
testPoints = testPoints'; % 转置张量

% 构建网络
numPoints = size(trainPoints, 2); % 获取点云数量
inputSize = size(trainPoints, 1); % 获取输入大小
outputSize = numel(unique(trainLabels)); % 获取输出大小

inputLayer = imageInputLayer([1 1 inputSize], 'Normalization', 'none'); % 输入层
transformLayer = pointwiseSpatialTransformLayer('STN'); % 变换层
convLayer1 = convolution2dLayer([1 64], 64, 'Padding', [0 0], 'Name', 'conv1'); % 卷积层1
bnLayer1 = batchNormalizationLayer('Name', 'bn1'); % 批归一化层1
reluLayer1 = reluLayer('Name', 'relu1'); % ReLU层1
maxpoolLayer1 = maxPooling2dLayer([numPoints 1], 'Name', 'maxpool1'); % 最大池化层1
convLayer2 = convolution2dLayer([1 1], 128, 'Padding', [0 0], 'Name', 'conv2'); % 卷积层2
bnLayer2 = batchNormalizationLayer('Name', 'bn2'); % 批归一化层2
reluLayer2 = reluLayer('Name', 'relu2'); % ReLU层2
convLayer3 = convolution2dLayer([1 1], 1024, 'Padding', [0 0], 'Name', 'conv3'); % 卷积层3
bnLayer3 = batchNormalizationLayer('Name', 'bn3'); % 批归一化层3
reluLayer3 = reluLayer('Name', 'relu3'); % ReLU层3
maxpoolLayer2 = maxPooling2dLayer([numPoints 1], 'Name', 'maxpool2'); % 最大池化层2
fcLayer1 = fullyConnectedLayer(512, 'Name', 'fc1'); % 全连接层1
bnLayer4 = batchNormalizationLayer('Name', 'bn4'); % 批归一化层4
reluLayer4 = reluLayer('Name', 'relu4'); % ReLU层4
fcLayer2 = fullyConnectedLayer(256, 'Name', 'fc2'); % 全连接层2
bnLayer5 = batchNormalizationLayer('Name', 'bn5'); % 批归一化层5
reluLayer5 = reluLayer('Name', 'relu5'); % ReLU层5
fcLayer3 = fullyConnectedLayer(outputSize, 'Name', 'fc3'); % 全连接层3
softmaxLayer = softmaxLayer('Name', 'softmax'); % Softmax层
classificationLayer = classificationLayer('Name', 'classification'); % 分类层

layers = [
    inputLayer
    transformLayer
    convLayer1
    bnLayer1
    reluLayer1
    maxpoolLayer1
    convLayer2
    bnLayer2
    reluLayer2
    convLayer3
    bnLayer3
    reluLayer3
    maxpoolLayer2
    fcLayer1
    bnLayer4
    reluLayer4
    fcLayer2
    bnLayer5
    reluLayer5
    fcLayer3
    softmaxLayer
    classificationLayer
]; % 网络层次结构

% 训练网络
options = trainingOptions('adam', ...
    'ExecutionEnvironment', 'auto', ...
    'MaxEpochs', 20, ...
    'MiniBatchSize', 32, ...
    'Shuffle', 'every-epoch', ...
    'Verbose', true, ...
    'Plots', 'training-progress'); % 设置训练选项

net = trainNetwork(trainPoints, categorical(trainLabels), layers, options); % 训练网络

% 测试网络
YPred = classify(net, testPoints); % 对测试数据进行分类
accuracy = sum(YPred == categorical(testLabels))/numel(testLabels); % 计算准确率

需要的库包括：Deep Learning Toolbox，Computer Vision Toolbox。