An Improved Algorithm Robust to Illumination Variations for Reconstructing Point Cloud Models from Images

Reconstructing 3D point cloud models from image sequences tends to be impacted by illumination variations and textureless cases in images, resulting in missing parts or uneven distribution of retrieved points. To improve the reconstructing completeness, this work proposes an enhanced similarity metric which is robust to illumination variations among images during the dense diffusions to push the seed-and-expand reconstructing scheme to a further extent. This metric integrates the zero-mean normalized cross-correlation coefficient of illumination and that of texture information which respectively weakens the influence of illumination variations and textureless cases. Incorporated with disparity gradient and confidence constraints, the candidate image features are diffused to their neighborhoods for dense 3D points recovering. We illustrate the two-phase results of multiple datasets and evaluate the robustness of proposed algorithm to illumination variations. Experiments show that ours recovers 10.0% more points, on average, than comparing methods in illumination varying scenarios and achieves better completeness with comparative accuracy.