资 源 简 介
Sparse bundle adjustment is widely used in many computer
vision applications. In this project, we develop a
method for performing bundle adjustments using the L1
norm. The method performs better for both synthetically generated and real data sets in the presence of outliers or Laplacian noise compared with the L2 norm bundle adjustment, and the method is efficient among the state of the art L1 minimization methods.
To get the source code, please click Downloads in the navigation bar and download the zip file web_code_release_0.1.zip. After you get the source, please follow the ReadMe file to run the code.
文 件 列 表
web_code_release
bundle.m
compute_res_inlier_l1.m
compute_res_inlier_l2.m
compute_res_max.m
compute_update_interior.m
compute_update_interior2.m
compute_update_sparse_l1.m
demo.m
dino.mat
l1decode_pd.m
license.txt
linprog2.m
optimset2.m
pflat.m
randelement.m
randlap.m
randomdata.m
randrot.m
ReadMe.pdf
sbal1.m
SBAL1.pdf
updateP.m
