稀疏束調整實現c/C++

Question

有人可以向我解釋下列函數等待什麼參數以及它們用於什麼？ 我有問題了解給定的描述。我想我通過了導致內存異常的錯誤參數。可悲的是，異常不能被調試，因爲它是來自庫的外部代碼。

此功能用於計算頂點的3D位置，只給出這些點在不同視圖中顯示3D場景的各種圖像上的投影。

這裏的函數頭：

int sba_motstr_levmar_x(

const int n, /* number of points */ 
const int ncon,/* number of points (starting from the 1st) whose parameters should not be modified. 
       * All B_ij (see below) with i<ncon are assumed to be zero 
       */ 
const int m, /* number of images */ 
const int mcon,/* number of images (starting from the 1st) whose parameters should not be modified. 
          * All A_ij (see below) with j<mcon are assumed to be zero 
          */ 


char *vmask, /* visibility mask: vmask[i, j]=1 if point i visible in image j, 0 otherwise. nxm */ 
double *p, /* initial parameter vector p0: (a1, ..., am, b1, ..., bn). 
       * aj are the image j parameters, bi are the i-th point parameters, 
       * size m*cnp + n*pnp 
       */ 
const int cnp,/* number of parameters for ONE camera; e.g. 6 for Euclidean cameras */ 
const int pnp,/* number of parameters for ONE point; e.g. 3 for Euclidean points */ 
double *x, /* measurements vector: (x_11^T, .. x_1m^T, ..., x_n1^T, .. x_nm^T)^T where 
       * x_ij is the projection of the i-th point on the j-th image. 
       * NOTE: some of the x_ij might be missing, if point i is not visible in image j; 
       * see vmask[i, j], max. size n*m*mnp 
       */ 
double *covx, /* measurements covariance matrices: (Sigma_x_11, .. Sigma_x_1m, ..., Sigma_x_n1, .. Sigma_x_nm), 
       * where Sigma_x_ij is the mnp x mnp covariance of x_ij stored row-by-row. Set to NULL if no 
       * covariance estimates are available (identity matrices are implicitly used in this case). 
       * NOTE: a certain Sigma_x_ij is missing if the corresponding x_ij is also missing; 
       * see vmask[i, j], max. size n*m*mnp*mnp 
       */ 
const int mnp,/* number of parameters for EACH measurement; usually 2 */ 
void (*func)(double *p, struct sba_crsm *idxij, int *rcidxs, int *rcsubs, double *hx, void *adata), 
              /* functional relation describing measurements. Given a parameter vector p, 
              * computes a prediction of the measurements \hat{x}. p is (m*cnp + n*pnp)x1, 
              * \hat{x} is (n*m*mnp)x1, maximum 
              * rcidxs, rcsubs are max(m, n) x 1, allocated by the caller and can be used 
              * as working memory 
              */ 
void (*fjac)(double *p, struct sba_crsm *idxij, int *rcidxs, int *rcsubs, double *jac, void *adata), 
              /* function to evaluate the sparse Jacobian dX/dp. 
              * The Jacobian is returned in jac as 
              * (dx_11/da_1, ..., dx_1m/da_m, ..., dx_n1/da_1, ..., dx_nm/da_m, 
              * dx_11/db_1, ..., dx_1m/db_1, ..., dx_n1/db_n, ..., dx_nm/db_n), or 
              * (using HZ's notation), 
              * jac=(A_11, B_11, ..., A_1m, B_1m, ..., A_n1, B_n1, ..., A_nm, B_nm) 
              * Notice that depending on idxij, some of the A_ij and B_ij might be missing. 
              * Note also that A_ij and B_ij are mnp x cnp and mnp x pnp matrices resp. and 
              * should be stored in jac in row-major order. 
              * rcidxs, rcsubs are max(m, n) x 1, allocated by the caller and can be used 
              * as working memory 
              * 
              * If NULL, the Jacobian is approximated by repetitive func calls and finite 
              * differences. This is computationally inefficient and thus NOT recommended. 
              */ 
void *adata,  /* pointer to possibly additional data, passed uninterpreted to func, fjac */ 

const int itmax, /* I: maximum number of iterations. itmax==0 signals Jacobian verification followed by immediate return */ 
const int verbose, /* I: verbosity */ 
const double opts[SBA_OPTSSZ], 
        /* I: minim. options [\mu, \epsilon1, \epsilon2, \epsilon3, \epsilon4]. Respectively the scale factor for 
        * initial \mu, stopping thresholds for ||J^T e||_inf, ||dp||_2, ||e||_2 and (||e||_2-||e_new||_2)/||e||_2 
        */ 
double info[SBA_INFOSZ] 
        /* O: information regarding the minimization. Set to NULL if don't care 
        * info[0]=||e||_2 at initial p. 
        * info[1-4]=[ ||e||_2, ||J^T e||_inf, ||dp||_2, mu/max[J^T J]_ii ], all computed at estimated p. 
        * info[5]= # iterations, 
        * info[6]=reason for terminating: 1 - stopped by small gradient J^T e 
        *         2 - stopped by small dp 
        *         3 - stopped by itmax 
        *         4 - stopped by small relative reduction in ||e||_2 
        *         5 - stopped by small ||e||_2 
        *         6 - too many attempts to increase damping. Restart with increased mu 
        *         7 - stopped by invalid (i.e. NaN or Inf) "func" values; a user error 
        * info[7]= # function evaluations 
        * info[8]= # Jacobian evaluations 
        * info[9]= # number of linear systems solved, i.e. number of attempts for reducing error 
        */ 

）

爲了更好的閱讀，請訪問此鏈接，滾動到 「可用的功能」：

http://www.ics.forth.gr/~lourakis/sba/#documentation

這是als o這個圖書館的官方主頁。

Answer 1

通常，束調整是迭代技術，需要對外部攝像機參數進行合理的初始估計。你有嗎？如果不是的話，Levenberg-Marquardt優化器可以收斂到當地的最小值，最終會得到無用的結果。您可能會感興趣的其他資源可以在這裏找到。我不認爲他們需要良好的初始估計值：

資源我：

http://www.maths.lth.se/vision/downloads/

兩個視圖幾何估計與離羣

C++尋找兩個相對方向代碼校準 相機存在異常值。所獲得的解決方案在內部數量最大化的意義上是最優的。

資源II：

http://www.halcon.com/halcon/download/documentation/reference-9.0/hdevelop/toc_tools_stereo.html

vector_to_rel_pose

計算給定圖像點對應2個 相機和已知的相機參數 和重建的3D空間中的點之間的相對方位。