- （1）motion and structure
sba_motstr_levmar(), sba_motstr_levmar_x():
- Resp. simple and expert driver for full motion and structure BA.
- (2)only motion
- sba_mot_levmar(), sba_mot_levmar_x():
- Resp. simple and expert driver for motion only BA. Strictly speaking, this is not BA since structure is kept unmodified. However, this function is very useful when dealing with problems involving camera resectioning, i.e. pose estimation from known 3D-2D correspondences.
- (3)only structure
sba_str_levmar(), sba_str_levmar_x():
- Resp. simple and expert driver for structure only BA. Again, this is not real BA since motion is kept unmodified. This function can, for example, be useful when dealing with intersection problems, i.e. reconstructing 3D points seen in a set of extrinsically calibrated images.
/**//* simple drivers */
extern int
sba_motstr_levmar(const int n, const int m, const int mcon, char *vmask, double *p, const int cnp, const int pnp,
double *x, double *covx, const int mnp,
void (*proj)(int j, int i, double *aj, double *bi, double *xij, void *adata),
void (*projac)(int j, int i, double *aj, double *bi, double *Aij, double *Bij, void *adata),
void *adata, const int itmax, const int verbose, const double opts[SBA_OPTSSZ], double info[SBA_INFOSZ]);
extern int
sba_mot_levmar(const int n, const int m, const int mcon, char *vmask, double *p, const int cnp,
double *x, double *covx, const int mnp,
void (*proj)(int j, int i, double *aj, double *xij, void *adata),
void (*projac)(int j, int i, double *aj, double *Aij, void *adata),
void *adata, const int itmax, const int verbose, const double opts[SBA_OPTSSZ], double info[SBA_INFOSZ]);
extern int
sba_str_levmar(const int n, const int m, char *vmask, double *p, const int pnp,
double *x, double *covx, const int mnp,
void (*proj)(int j, int i, double *bi, double *xij, void *adata),
void (*projac)(int j, int i, double *bi, double *Bij, void *adata),
void *adata, const int itmax, const int verbose, const double opts[SBA_OPTSSZ], double info[SBA_INFOSZ]);
/**//* expert drivers */
extern int
sba_motstr_levmar_x(const int n, const int m, const int mcon, char *vmask, double *p, const int cnp, const int pnp,
double *x, double *covx, const int mnp,
void (*func)(double *p, struct sba_crsm *idxij, int *rcidxs, int *rcsubs, double *hx, void *adata),
void (*fjac)(double *p, struct sba_crsm *idxij, int *rcidxs, int *rcsubs, double *jac, void *adata),
void *adata, const int itmax, const int verbose, const double opts[SBA_OPTSSZ], double info[SBA_INFOSZ]);
extern int
sba_mot_levmar_x(const int n, const int m, const int mcon, char *vmask, double *p, const int cnp,
double *x, double *covx, const int mnp,
void (*func)(double *p, struct sba_crsm *idxij, int *rcidxs, int *rcsubs, double *hx, void *adata),
void (*fjac)(double *p, struct sba_crsm *idxij, int *rcidxs, int *rcsubs, double *jac, void *adata),
void *adata, const int itmax, const int verbose, const double opts[SBA_OPTSSZ], double info[SBA_INFOSZ]);
extern int
sba_str_levmar_x(const int n, const int m, char *vmask, double *p, const int pnp,
double *x, double *covx, const int mnp,
void (*func)(double *p, struct sba_crsm *idxij, int *rcidxs, int *rcsubs, double *hx, void *adata),
void (*fjac)(double *p, struct sba_crsm *idxij, int *rcidxs, int *rcsubs, double *jac, void *adata),
void *adata, const int itmax, const int verbose, const double opts[SBA_OPTSSZ], double info[SBA_INFOSZ]);
/**//* Bundle adjustment on camera and structure parameters
* using sparse Levenberg-Marquardt
*
* Returns the number of iterations (>=0) if successfull, SBA_ERROR if failed
*/
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
*/
)
image stitching often used function sba_motstr_levmar_x
posted on 2011-01-28 10:55
noBugnoGain 阅读(968)
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