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Image-based reconstruction is devoted to recovering the 3D point cloud models of target objects from scene images photographed at different viewpoints, and the existing methods often produce a large number of redundant background points, which causes inconvenience to 3D modeling or other related applications. To solve this issue, this work proposes an improved framework that combines image segmentation in the point cloud retrieving procedure, so as it only reconstructs the objects of interest in a scene. This framework provides two options for foreground object segmentation, and users can determine the appropriate method to obtain accurate segmentation for different scenes. Then, the feature matches are extracted from the segmented images, and the point cloud model is recovered via two phases of dense diffusion, feature diffusion and patch diffusion. In the diffusion stage, we introduce a new normalized metric that deals with both the illumination change and low texture case to enhance the robustness of the reconstruction. The experimental results show that proposed framework can effectively avoid reconstructing the irrelevant background data while outputting more even and detailed point cloud models. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]