我遇到了同样的问题，并找到了一个合适的解决方案：github Emgu.CV.Example DrawMatches.cs在其中一切都工作。
我修改了代码和方法FindMatch看起来像这样：

public static void FindMatch(Mat modelImage, Mat observedImage, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, VectorOfVectorOfDMatch matches, out Mat mask, out Mat homography)
{
    int k = 2;
    double uniquenessThreshold = 0.80;
    homography = null;
    modelKeyPoints = new VectorOfKeyPoint();
    observedKeyPoints = new VectorOfKeyPoint();
    using (UMat uModelImage = modelImage.GetUMat(AccessType.Read))
    using (UMat uObservedImage = observedImage.GetUMat(AccessType.Read))
    {
        var featureDetector = new ORBDetector(9000);
        Mat modelDescriptors = new Mat();
        featureDetector.DetectAndCompute(uModelImage, null, modelKeyPoints, modelDescriptors, false);
        Mat observedDescriptors = new Mat();
        featureDetector.DetectAndCompute(uObservedImage, null, observedKeyPoints, observedDescriptors, false);
        using (var matcher = new BFMatcher(DistanceType.Hamming, false))
        {
            matcher.Add(modelDescriptors);

            matcher.KnnMatch(observedDescriptors, matches, k, null);
            mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
            mask.SetTo(new MCvScalar(255));
            Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, mask);

            int nonZeroCount = CvInvoke.CountNonZero(mask);
            if (nonZeroCount >= 4)
            {
                nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints,
                    matches, mask, 1.5, 20);
                if (nonZeroCount >= 4)
                    homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints,
                        observedKeyPoints, matches, mask, 2);
            }
        }
    }
}

使用方法：

var model = new Mat(@"image0.jpg");
var scene = new Mat(@"image1.jpg");
Mat result = new Mat();
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
var matches = new VectorOfVectorOfDMatch();
Mat mask;
Mat homography;
FindMatch(model, scene, out modelKeyPoints, out observedKeyPoints, matches, out mask, out homography);
CvInvoke.WarpPerspective(scene, result, homography, model.Size, Inter.Linear, Warp.InverseMap);

结果：

如果你想看这个过程使用下一个代码：

public static Mat Draw(Mat modelImage, Mat observedImage)
{
    Mat homography;
    VectorOfKeyPoint modelKeyPoints;
    VectorOfKeyPoint observedKeyPoints;
    using (VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch())
    {
        Mat mask;
        FindMatch(modelImage, observedImage, out modelKeyPoints, out observedKeyPoints, matches, out mask, out homography);
        Mat result = new Mat();
        Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints,
            matches, result, new MCvScalar(255, 0, 0), new MCvScalar(0, 0, 255), mask);

        if (homography != null)
        {
            var imgWarped = new Mat();
            CvInvoke.WarpPerspective(observedImage, imgWarped, homography, modelImage.Size, Inter.Linear, Warp.InverseMap);
            Rectangle rect = new Rectangle(Point.Empty, modelImage.Size);
            var pts = new PointF[]
            {
                  new PointF(rect.Left, rect.Bottom),
                  new PointF(rect.Right, rect.Bottom),
                  new PointF(rect.Right, rect.Top),
                  new PointF(rect.Left, rect.Top)
            };

            pts = CvInvoke.PerspectiveTransform(pts, homography);
            var points = new Point[pts.Length];
            for (int i = 0; i < points.Length; i++)
                points[i] = Point.Round(pts[i]);

            using (var vp = new VectorOfPoint(points))
            {
                CvInvoke.Polylines(result, vp, true, new MCvScalar(255, 0, 0, 255), 5);
            }
        }
        return result;
    }
}

使用方法：

var model = new Mat(@"image0.jpg");
var scene = new Mat(@"image1.jpg");
var result = Draw(model, scene);

结果：

解决方案

问题一

最大的问题其实很简单，我在匹配时不小心翻转了模型和测试描述符：

matcher.Add(imgTest.Descriptors);
matcher.KnnMatch(imgModel.Descriptors, matches, 1, null);

但是，如果您查看这些函数的文档，您将看到您必须添加模型并与测试映像进行匹配。

matcher.Add(imgModel.Descriptors);
matcher.KnnMatch(imgTest.Descriptors, matches, 1, null);

问题二

我不知道为什么到现在为止，但Features2DToolbox.GetHomographyMatrixFromMatchedFeatures似乎是坏的，我的单应性总是错误的，扭曲的图像在一个奇怪的方式（类似于上述的例子）。
为了解决这个问题，我直接使用了OpenCV FindHomography(srcPoints, destPoints, method)的 Package 器调用。为了能够做到这一点，我必须编写一个小助手来以正确的格式获取我的数据结构：

public static Mat GetHomography(VectorOfKeyPoint keypointsModel, VectorOfKeyPoint keypointsTest, List<MDMatch[]> matches)
{
  MKeyPoint[] kptsModel = keypointsModel.ToArray();
  MKeyPoint[] kptsTest = keypointsTest.ToArray();

  PointF[] srcPoints = new PointF[matches.Count];
  PointF[] destPoints = new PointF[matches.Count];

  for (int i = 0; i < matches.Count; i++)
  {
    srcPoints[i] = kptsModel[matches[i][0].TrainIdx].Point;
    destPoints[i] = kptsTest[matches[i][0].QueryIdx].Point;
  }

  Mat homography = CvInvoke.FindHomography(srcPoints, destPoints, Emgu.CV.CvEnum.HomographyMethod.Ransac);

  //PrintMatrix(homography);

  return homography;
}

结果

现在一切正常，正如预期的那样：