Key technologies of light field capture for 3D reconstruction in microscopic scene

Wang, Yu; Ji, Xiangyang; Dai, Qionghai

doi:10.1007/s11432-010-4045-2

Cited by 4 publications

(1 citation statement)

References 41 publications

(43 reference statements)

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“…In fact, recovering high dimensional data exactly from low dimensional samples exactly has been an ill-conditional problem without an exact solution so far, because there must be information missing for the reconstruction of data having higher dimensions. Nevertheless, there has been much excellent albeit struggling work on the topic in the past such as those methods proposed in [8][9][10][11]. However, most of them are not effective for dealing with difficult cases such as textureless regions, outliers, and detail loss.…”

Section: Introductionmentioning

confidence: 99%

Robust dense reconstruction by range merging based on confidence estimation

Chen

Hao

et al. 2016

Sci. China Inf. Sci.

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Although the stereo matching problem has been extensively studied during the past decades, automatically computing a dense 3D reconstruction from several multiple views is still a difficult task owing to the problems of textureless regions, outliers, detail loss, and various other factors. In this paper, these difficult problems are handled effectively by a robust model that outputs an accurate and dense reconstruction as the final result from an input of multiple images captured by a normal camera. First, the positions of the camera and sparse 3D points are estimated by a structure-from-motion algorithm and we compute the range map with a confidence estimation for each image in our approach. Then all the range maps are integrated into a fine point cloud data set. In the final step we use a Poisson reconstruction algorithm to finish the reconstruction. The major contributions of the work lie in the following points: effective range-computation and confidence-estimation methods are proposed to handle the problems of textureless regions, outliers and detail loss. Then, the range maps are merged into the point cloud data in terms of a confidence-estimation. Finally, Poisson reconstruction algorithm completes the dense mesh. In addition, texture mapping is also implemented as a post-processing work for obtaining good visual effects. Experimental results are presented to demonstrate the effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%