一、技术原理与实现框架
单目深度估计通过神经网络直接从单视角图像预测深度图，核心流程包括：
- 数据输入：RGB图像 → 网络前向传播 → 深度图输出
- 特征提取：编码器提取多尺度特征（如ResNet、VGG）
- 特征融合：跳跃连接（Skip Connection）结合浅层细节与深层语义
- 深度回归：解码器生成密集深度预测
核心挑战：
- 单目深度的病态性（同一图像对应多组可能深度）
- 缺乏几何约束（需依赖数据驱动建模）
二、MATLAB实现步骤
1. 数据准备与预处理
% 加载KITTI数据集（需提前下载）
dataFolder = 'path_to_kitti_dataset';
imds = imageDatastore(fullfile(dataFolder,'image_2'), ...'IncludeSubfolders',true,'LabelSource','foldernames');
depthds = imageDatastore(fullfile(dataFolder,'proj_depth','groundtruth'),...'IncludeSubfolders',true,'LabelSource','foldernames');% 数据增强
augmenter = imageDataAugmenter('RandRotation',[0,10],...'RandXReflection',true,'RandYReflection',true);
augimds = augmentedImageDatastore([256 512], imds, 'DataAugmentation', augmenter);% 划分训练集/验证集
[imdsTrain, imdsVal] = splitEachLabel(imds,0.8,'randomized');
2. 网络架构设计（基于FCRN）
layers = [imageInputLayer([256 512 3](@ref)...convolution2dLayer(7,64,'Padding','same')...batchNormalizationLayer...reluLayer...% 编码器部分（残差块）residualBlock(64,64)...residualBlock(64,128,'stride',2)...residualBlock(128,256,'stride',2)...% 解码器部分（上采样+跳跃连接）transposedConv2dLayer(3,128,'Stride',2,'Cropping','same')...concatenationLayer(2,1,3)@ref) % 跳跃连接第3层特征convolution2dLayer(3,64,'Padding','same')...transposedConv2dLayer(3,32,'Stride',2,'Cropping','same')...convolution2dLayer(3,1,'Padding','same')...regressionLayer]; % 深度回归输出% 定义残差块函数
function layers = residualBlock(in_channels,out_channels,stride)layers = [convolution2dLayer(3,out_channels,'Stride',stride,'Padding','same')...batchNormalizationLayer...reluLayer...convolution2dLayer(3,out_channels,'Stride',1,'Padding','same')...batchNormalizationLayer...additionLayer(2)@ref) % 残差连接reluLayer];
end
3. 训练配置与执行
options = trainingOptions('adam',...'MaxEpochs',100,...'MiniBatchSize',16,@ref)...'InitialLearnRate',1e-4,@ref)...'LearnRateSchedule','piecewise',...'LearnRateDropFactor',0.1,@ref)...'LearnRateDropPeriod',20,@ref)...'Shuffle','every-epoch',...'ValidationData',{imdsVal,depthdsVal},...'ValidationFrequency',30,@ref)...'Verbose',false,@ref)...'Plots','training-progress');net = trainNetwork(imdsTrain,depthdsTrain,layers,options);
4. 模型评估与可视化
% 测试集预测
YPred = classify(net,imdsTest);% 计算评估指标
rmse = sqrt(mean((YPred - depthdsTest.Labels).^2));
abs_rel = mean(abs(YPred - depthdsTest.Labels)./depthdsTest.Labels);
disp(['RMSE: ',num2str(rmse),', Abs Rel: ',num2str(abs_rel)]);% 深度图可视化
figure;
montage({im2double(imdsTest.Images{1}),YPred(:,:,1)});
title('Input Image vs Predicted Depth');
三、关键技术优化策略
-
多尺度特征融合
- 在编码器中添加空洞卷积（Dilated Convolution）扩大感受野
dilatedConv = convolution2dLayer(3,64,'DilationFactor',2,'Padding','same'); -
几何约束注入
- 添加SSIM损失增强结构相似性
layers(end-1) = ssimLossLayer('Name','ssim_loss'); % 自定义SSIM损失层 -
动态范围压缩
- 对数变换缓解深度值分布不均
inputLayer = imageInputLayer([256 512 3](@ref)...'Normalization','log');
四、完整代码
% 完整训练脚本（保存为trainDepthNet.m）
clear; clc;
load('pretrained_resnet50.mat'); % 加载预训练权重% 修改最后全连接层为回归层
layers = resnet50.Layers;
layers(end-2) = convolution2dLayer(1,1,'Name','depth_output');
layers(end) = regressionLayer('Name','output');% 迁移学习配置
options = trainingOptions('adam',...'InitialLearnRate',1e-5,@ref)...'MiniBatchSize',8,@ref)...'Shuffle','every-epoch',...'ValidationData',{imdsVal,depthdsVal},...'MaxEpochs',50);net = trainNetwork(imdsTrain,depthdsTrain,layers,options);
参考代码 使用matlab搭建神经网络，从单目图像中估计深度值 youwenfan.com/contentcnc/84457.html
参考文献
[1] FCRN网络在KITTI数据集上的实现细节
[2] 单目深度估计的几何约束方法
[3] MATLAB深度学习工具箱官方文档
[4] NYU Depth数据集预处理指南
[5] 混合精度训练在MATLAB中的应用