From Event: SPIE Commercial + Scientific Sensing and Imaging, 2018
This paper presents a computationally efficient pipeline to achieve 3D point cloud reconstruction from video sequences. This pipeline involves a key frame selection step to improve the computational efficiency by generating reliable depth information from pair-wise frames. An outlier removal step is then applied in order to further improve the computational efficiency. The reconstruction is achieved based on a new absolute camera pose recovery approach in a computationally efficient manner. This pipeline is devised for both sparse and dense 3D reconstruction. The results obtained from video sequences exhibit higher computational efficiency and lower re-projection errors of the introduced pipeline compared to the existing pipelines.
Chih-Hsiang Chang and Nasser Kehtarnavaz, "A computationally efficient pipeline for 3D point cloud reconstruction from video sequences," Proc. SPIE 10670, Real-Time Image and Video Processing 2018, 106700B (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 16, 2018; Published: 14 May 2018); https://doi.org/10.1117/12.2302674.
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