Colmap学习二：前端部分（特征点提取、匹配与剔除）

本文主要是介绍Colmap学习二：前端部分（特征点提取、匹配与剔除），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

1 Colmap特征提取和描述

colmap一般使用sift-gpu，涉及的知识：

1.1 图像金字塔：DOG图像金字塔，再找极值

1.2 SIFT特征检测与描述：确定主方向和128维的描述子，再归一化(消除光照的影响)。

其中由于Euclidean distance 容易受较大值的影响，使用
Hellinger distance更稳定。colmap先对sift的结果进行L1 normalize，再对每一个元素求平方根，得到的结果便是L2 normalized。在论文中给出了使用RootSIFT匹配的结果优于SIFT

1.3 colmap代码细节参数

Octave，Sublevel，模糊尺度，极值筛选阈值，初始直方图的平滑次数，描述子的归一化，DSP-Sift特征等。
直接看源码：在这里插入图片描述

1.4 colmap特征点储存格式，便于后续自己改feature

feature/types.h可以看到，2，4，6参数的形式都可以：


struct FeatureKeypoint {FeatureKeypoint();FeatureKeypoint(const float x, const float y);FeatureKeypoint(const float x, const float y, const float scale,const float orientation);FeatureKeypoint(const float x, const float y, const float a11,const float a12, const float a21, const float a22);static FeatureKeypoint FromParameters(const float x, const float y,const float scale_x,const float scale_y,const float orientation,const float shear);// Rescale the feature location and shape size by the given scale factor.void Rescale(const float scale);void Rescale(const float scale_x, const float scale_y);// Compute similarity shape parameters from affine shape.float ComputeScale() const;float ComputeScaleX() const;float ComputeScaleY() const;float ComputeOrientation() const;float ComputeShear() const;// Location of the feature, with the origin at the upper left image corner,// i.e. the upper left pixel has the coordinate (0.5, 0.5).float x;float y;// Affine shape of the feature.float a11;float a12;float a21;float a22;
};

base/database.cc可以看到默认为6（也就是ASIFT算法）

FeatureKeypointsBlob FeatureKeypointsToBlob(const FeatureKeypoints& keypoints) {const FeatureKeypointsBlob::Index kNumCols = 6;FeatureKeypointsBlob blob(keypoints.size(), kNumCols);for (size_t i = 0; i < keypoints.size(); ++i) {blob(i, 0) = keypoints[i].x;blob(i, 1) = keypoints[i].y;blob(i, 2) = keypoints[i].a11;blob(i, 3) = keypoints[i].a12;blob(i, 4) = keypoints[i].a21;blob(i, 5) = keypoints[i].a22;}return blob;
}FeatureKeypoints FeatureKeypointsFromBlob(const FeatureKeypointsBlob& blob) {FeatureKeypoints keypoints(static_cast<size_t>(blob.rows()));if (blob.cols() == 2) {for (FeatureKeypointsBlob::Index i = 0; i < blob.rows(); ++i) {keypoints[i] = FeatureKeypoint(blob(i, 0), blob(i, 1));}} else if (blob.cols() == 4) {for (FeatureKeypointsBlob::Index i = 0; i < blob.rows(); ++i) {keypoints[i] =FeatureKeypoint(blob(i, 0), blob(i, 1), blob(i, 2), blob(i, 3));}} else if (blob.cols() == 6) {for (FeatureKeypointsBlob::Index i = 0; i < blob.rows(); ++i) {keypoints[i] = FeatureKeypoint(blob(i, 0), blob(i, 1), blob(i, 2),blob(i, 3), blob(i, 4), blob(i, 5));}} else {LOG(FATAL) << "Keypoint format not supported";}return keypoints;
}FeatureMatchesBlob FeatureMatchesToBlob(const FeatureMatches& matches) {const FeatureMatchesBlob::Index kNumCols = 2;FeatureMatchesBlob blob(matches.size(), kNumCols);for (size_t i = 0; i < matches.size(); ++i) {blob(i, 0) = matches[i].point2D_idx1;blob(i, 1) = matches[i].point2D_idx2;}return blob;
}FeatureMatches FeatureMatchesFromBlob(const FeatureMatchesBlob& blob) {CHECK_EQ(blob.cols(), 2);FeatureMatches matches(static_cast<size_t>(blob.rows()));for (FeatureMatchesBlob::Index i = 0; i < blob.rows(); ++i) {matches[i].point2D_idx1 = blob(i, 0);matches[i].point2D_idx2 = blob(i, 1);}return matches;
}

2 Colmap特征匹配算法

2.1 相似度判断准则：余弦相似度

2.2 五种匹配算法

暴力匹配（遍历费时），顺序匹配（序列图像用）、词汇树匹配（兼顾时效）、空间匹配（有额外的空间信息用）和转移匹配：
在这里插入图片描述

上图的Custom为自定义，自己去定义哪几张图像匹配哪几张不匹配，算是一种人工调参手段。

colmap中的Sequential Matching
colmap中Spatial Matching增添位姿信息
colmap各种匹配的使用体验

2.3 几何验证算法：剔除outliers

对于标定相机，用E/F H/F H/E的内点个数比值来决定选用的剔除模型

 // Determine inlier ratios of different models.const double E_F_inlier_ratio =static_cast<double>(E_report.support.num_inliers) /F_report.support.num_inliers;const double H_F_inlier_ratio =static_cast<double>(H_report.support.num_inliers) /F_report.support.num_inliers;const double H_E_inlier_ratio =static_cast<double>(H_report.support.num_inliers) /E_report.support.num_inliers;

对于未标定相机，利用仅可以得到的F矩阵（E无法得到

 // Uncalibrated configuration.num_inliers = F_report.support.num_inliers;best_inlier_mask = &F_report.inlier_mask;if (H_F_inlier_ratio > options.max_H_inlier_ratio) {config = ConfigurationType::PLANAR_OR_PANORAMIC;if (H_report.support.num_inliers > num_inliers) {num_inliers = H_report.support.num_inliers;best_inlier_mask = &H_report.inlier_mask;}} else {config = ConfigurationType::UNCALIBRATED;}} else if (H_report.success &&H_report.support.num_inliers >= options.min_num_inliers) {num_inliers = H_report.support.num_inliers;best_inlier_mask = &H_report.inlier_mask;config = ConfigurationType::PLANAR_OR_PANORAMIC;} else {config = ConfigurationType::DEGENERATE;return;}

Guide Matching：由网课上说的是，图1点x在图2的极线Fx上,但是由于误差可能在其附近，于是设置了一个极线的上下阈值。想更全面的了解，可以参考OpenMVG的匹配对几何验证。
GeometricAdjacencyMatrix：无向图，一个矩阵。
– n*n矩阵（n为图像个数），0代表没有，其他数值代表有多少匹配。
– i行j列也就是图i∈n与图j∈n的匹配点个数。可知，对角位置为0（自己和自己无匹配）

2.4 匹配点存储格式

索引对（替换前端只要统一为以下输出格式就好了）

FeatureMatchesBlob FeatureMatchesToBlob(const FeatureMatches& matches) {const FeatureMatchesBlob::Index kNumCols = 2;FeatureMatchesBlob blob(matches.size(), kNumCols);for (size_t i = 0; i < matches.size(); ++i) {blob(i, 0) = matches[i].point2D_idx1;blob(i, 1) = matches[i].point2D_idx2;}return blob;
}FeatureMatches FeatureMatchesFromBlob(const FeatureMatchesBlob& blob) {CHECK_EQ(blob.cols(), 2);FeatureMatches matches(static_cast<size_t>(blob.rows()));for (FeatureMatchesBlob::Index i = 0; i < blob.rows(); ++i) {matches[i].point2D_idx1 = blob(i, 0);matches[i].point2D_idx2 = blob(i, 1);}return matches;
}

3. 动态物体剔除

剔除Mask以外的特征

Mask是动态目标的话，mask内部应该为黑色0
Mask是要保留的目标的话，mask外部为黑色0
colmap的以下函数仅将mask内的点输入到out_index中输出keypoints：

void MaskKeypoints(const Bitmap& mask, FeatureKeypoints* keypoints,FeatureDescriptors* descriptors) {size_t out_index = 0;BitmapColor<uint8_t> color;for (size_t i = 0; i < keypoints->size(); ++i) {if (!mask.GetPixel(static_cast<int>(keypoints->at(i).x),static_cast<int>(keypoints->at(i).y), &color) ||color.r == 0) {// Delete this keypoint by not copying it to the output.} else {// Retain this keypoint by copying it to the output index (in case this// index differs from its current position).if (out_index != i) {keypoints->at(out_index) = keypoints->at(i);for (int col = 0; col < descriptors->cols(); ++col) {(*descriptors)(out_index, col) = (*descriptors)(i, col);}}out_index += 1;}}keypoints->resize(out_index);descriptors->conservativeResize(out_index, descriptors->cols());
}