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I am currently trying to modify the stitcher pipeline to get something a little more robust for a specific purpose. I have only modified the feature extraction and the pairwise matching part. My code looks something close to what follows:

Ptr<detail::FeaturesFinder> features_finder_ = makePtr<detail::SurfFeaturesFinder>();
Ptr<detail::FeaturesMatcher> features_matcher_ = 
    makePtr<detail::BestOf2NearestMatcher>(try_use_gpu, 0.3f);
std::vector<detail::ImageFeatures> features_;
std::vector<UMat> feature_find_imgs;
(*features_finder_)(feature_find_imgs, features_);

int nmb_imgs = features_.size();
Ptr<DescriptorMatcher> matcher = BFMatcher::create(NORM_L2,false);
std::vector<detail::MatchesInfo> pairwise_matches(nmb_imgs * nmb_imgs);

for(int i=0; i<(int)features_.size()-1; ++i){
  detail::MatchesInfo matchInfos[2];

  // I know that the images come consecutively from a panorama therefore the following
  int idx1 = i;
  int idx2 = i+1;

  for(int j=0; j<2; ++j){

    vector<KeyPoint> & kp1 = features_[idx1].keypoints;
    vector<KeyPoint> & kp2 = features_[idx2].keypoints;
    Mat des1 = features_[idx1].descriptors.getMat(ACCESS_RW);
    Mat des2 = features_[idx2].descriptors.getMat(ACCESS_RW);

    std::vector<std::vector<DMatch> > matches;
    matcher->knnMatch(des2, des1, matches, 2);

    std::vector<DMatch> refined_matches;

   // find refined_matches from matches 

    if(refined_matches.size() < 4){
      swap(idx1,idx2);
      continue;
    }
    vector<KeyPoint> kp1Refined(refined_matches.size());
    vector<KeyPoint> kp2Refined(refined_matches.size());
    for(int i=0; i<(int)refined_matches.size(); ++i){
      kp1Refined[i] = kp1[refined_matches[i].trainIdx];
      kp2Refined[i] = kp2[refined_matches[i].queryIdx];
    }

    Mat kp1Mat(1, refined_matches.size(), CV_32FC2);
    Mat kp2Mat(1, refined_matches.size(), CV_32FC2);
    for(int i=0; i<(int)refined_matches.size(); ++i){
      kp1Mat.at<Vec2f>(0,i)[0] = kp1Refined[i].pt.x;
      kp1Mat.at<Vec2f>(0,i)[1] = kp1Refined[i].pt.y;
      kp2Mat.at<Vec2f>(0,i)[0] = kp2Refined[i].pt.x;
      kp2Mat.at<Vec2f>(0,i)[1] = kp2Refined[i].pt.y;
    }

    Mat mask;
    Mat H = findHomography(kp2Mat, kp1Mat, RANSAC, 1.0, mask);
    H.convertTo(matchInfos[j].H, CV_64F);
    matchInfos[j].matches = refined_matches;
    matchInfos[j].src_img_idx = idx1;
    matchInfos[j].dst_img_idx = idx2;
    matchInfos[j].confidence = 1.0;
    matchInfos[j].num_inliers = 0;
    matchInfos[j].inliers_mask.clear();
    for(int i=0; i<(int)refined_matches.size(); ++i){
      if(mask.at<uchar>(i,0) == 1)
        matchInfos[j].num_inliers++;
      matchInfos[j].inliers_mask.push_back(mask.at<uchar>(i,0));
    }

    pairwise_matches[idx1 * nmb_imgs + idx2] = matchInfos[j];

    swap(idx1,idx2);
  }
}

But this does not give at all the desired result. I did not touch the remaining part of the pipeline. If I test with two images the resulting images contains both images but weirdly warped and not all stitched together. I guess I am not constructing the MatchesInfo correctly, which the pipeline later uses to estimate the camera parameters, bundle adjustment and warping. How do I create the MatchesInfo correctly?