双目视觉三维重建代码 matlab

以下是一个简单的双目视觉三维重建的 MATLAB 代码示例：

% 读取左右相机的图像
leftImage = imread('left.png');
rightImage = imread('right.png');

% 将图像转换为灰度图
leftGray = rgb2gray(leftImage);
rightGray = rgb2gray(rightImage);

% 检测特征点并匹配
pointsLeft = detectSURFFeatures(leftGray);
pointsRight = detectSURFFeatures(rightGray);
[featuresLeft, pointsLeft] = extractFeatures(leftGray, pointsLeft);
[featuresRight, pointsRight] = extractFeatures(rightGray, pointsRight);
indexPairs = matchFeatures(featuresLeft, featuresRight);

% 获取匹配点的坐标
matchedPointsLeft = pointsLeft(indexPairs(:,1), :);
matchedPointsRight = pointsRight(indexPairs(:,2), :);

% 计算基础矩阵和本质矩阵
[F, epipolarInliers] = estimateFundamentalMatrix(matchedPointsLeft, matchedPointsRight);
E = cameraMatrix2EssentialMatrix(F, [0, 0, 1]);

% 从本质矩阵中恢复相机参数
[orientations, locations, validPoints] = relativeCameraPose(E, ...
    matchedPointsLeft(epipolarInliers, :), matchedPointsRight(epipolarInliers, :));

% 计算视差图并进行三维重建
disparityMap = disparity(leftGray, rightGray);
points3D = reconstructScene(disparityMap, stereoParams);

需要注意的是，此代码示例仅包含了基本的双目视觉三维重建流程，实际应用中可能需要进行更多的参数调整和优化。此外，还需要准备好适当的标定数据和相机参数，以便正确地计算基础矩阵、本质矩阵和相机姿态等信息。