Fast key-frame extraction for 3D reconstruction from a handheld video

Choi, Jong-Ho; Kwon, Soonchul; Son, Kwang Chul; Yoo, Jisang

doi:10.7236/ijasc.2016.5.4.1

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…Furthermore, the standard baseline between sequence frames should be sufficient to reduce the uncertainty of the depth calculation resulting from the triangulation method of the corresponding features in the 3D reconstruction process. Figure 4 illustrates that a short standard baseline increases measurement error compared to a long baseline (Ahmed, 2010;Choi, 2016). In this paper, the ratio coefficient of the corresponding key points between the two frames was used to evaluate the standard baseline between the frames to keyframes extraction, according to equation ( 7).…”

Section: Standard Baseline Between Sequence Framesmentioning

confidence: 99%

“…Therefore, keyframe extraction as a representative of all captured frames capable of overcoming the issues mentioned above is critical in the 3D modeling process. As previously stated, to reconstruct the 3D model using video frames, keyframes must be extracted for accurate geometric estimation of the 3D model (Choi, 2016). The following section reviews related works in keyframes extraction from a video frame sequence.…”

Section: Introductionmentioning

confidence: 99%

“…To avoid degeneracy conditions, the proposed method includes removing blur frames, applying an overlapping filter between frames, selecting an appropriate baseline between two frames, and utilizing the GRIC criterion (Hossein Pour, 2016). Following that, Choi et al (2016) present a method for extracting frames containing helpful information from a video captured by a handheld camera in their study. In their research, they propose an approach that combines extraction criteria based on determining the appropriate baseline between frames, frame jumping for fast search in the movie, GRIC geometric information criteria to calculate frame by frame homography and fundamental matrix, and removing blur frames (Choi, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Following that, Choi et al (2016) present a method for extracting frames containing helpful information from a video captured by a handheld camera in their study. In their research, they propose an approach that combines extraction criteria based on determining the appropriate baseline between frames, frame jumping for fast search in the movie, GRIC geometric information criteria to calculate frame by frame homography and fundamental matrix, and removing blur frames (Choi, 2016). Zhang et al (2017) present a fast approach to keyframe extraction and an optimal matching method based on geometric constraints of the path and flight direction.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Keyframes Extraction From Thermal Infrared Video Stream to Generate Dense Point Cloud of the Building's Facade

Motayyeb

Samadzadegan

Javan

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Keyframes extraction is required and effective for the 3D reconstruction of objects from a thermal video sequence to increase geometric accuracy, reduce the volume of aerial triangulation calculations, and generate the dense point cloud. The primary goal and focus of this paper are to assess the effect of keyframes extraction from the thermal infrared video sequence on the geometric accuracy of the dense point cloud generated. The method of keyframes extraction of thermal infrared video presented in this paper consists of three basic steps. (A) The ability to identify and remove blur frames from non-blur frames in a sequence of recorded frames. (B) The ability to apply the standard baseline condition between sequence frames to establish the overlap condition and prevent the creation of degeneracy conditions. (C) Evaluating degeneracy conditions and keyframes extraction using Geometric Robust Information Criteria (GRIC). The performance evaluation criteria for keyframes extraction in the generation of the thermal infrared dense point cloud in this paper are to assess the increase in density of the generated three-dimensional point cloud and reduce reprojection error. Based on the results and assessments presented in this paper, using keyframes increases the density of the thermal infrared dense point cloud by about 0.03% to 0.10% of points per square meter. It reduces the reprojection error by about 0.005% of pixels (2 times).

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Section: Standard Baseline Between Sequence Framesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effect of Keyframes Extraction From Thermal Infrared Video Stream to Generate Dense Point Cloud of the Building's Facade

Motayyeb

Samadzadegan

Javan

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

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A Keyframe Extraction Approach for 3D Videogrammetry Based on Baseline Constraints

Liu,

Hu,

Huang

2024

photogramm eng remote sensing

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In this paper, we propose a novel approach for the extraction of high-quality frames to enhance the fidelity of videogrammetry by combining fuzzy frames removal and baseline constraints. We first implement a gradient-based mutual information method to filter out low-quality frames while preserving the integrity of the videos. After frame pose estimation, the geometric properties of the baseline are constrained by three aspects to extract the keyframes: quality of relative orientation, baseline direction, and base to distance ratio. The three-dimensional model is then reconstructed based on these extracted keyframes. Experimental results demonstrate that our approach maintains a strong robustness throughout the aerial triangulation, leading to high levels of reconstruction precision across diverse video scenarios. Compared to other methods, this paper improves the reconstruction accuracy by more than 0.2 mm while simultaneously maintaining the completeness.

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Fast key-frame extraction for 3D reconstruction from a handheld video

Abstract: In order to reconstruct a 3D model in video sequences

Cited by 2 publications

References 13 publications

Effect of Keyframes Extraction From Thermal Infrared Video Stream to Generate Dense Point Cloud of the Building's Facade

Effect of Keyframes Extraction From Thermal Infrared Video Stream to Generate Dense Point Cloud of the Building's Facade

A Keyframe Extraction Approach for 3D Videogrammetry Based on Baseline Constraints

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