A computationally efficient pipeline for 3D point cloud reconstruction from video sequences

Chih-Hsiang Chang; Nasser Kehtarnavaz

doi:10.1117/12.2302674

14 May 2018 A computationally efficient pipeline for 3D point cloud reconstruction from video sequences

Chih-Hsiang Chang, Nasser Kehtarnavaz

Proceedings Volume 10670, Real-Time Image and Video Processing 2018; 106700B (2018) https://doi.org/10.1117/12.2302674
Event: SPIE Commercial + Scientific Sensing and Imaging, 2018, Orlando, FL, United States

Abstract

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.

Conference Presentation

Citation Download Citation

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 (14 May 2018); https://doi.org/10.1117/12.2302674

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