Efficient SfM for Oblique UAV Images: From Match Pair Selection to Geometrical Verification

Jiang, San; Jiang, Wanshou

doi:10.3390/rs10081246

Cited by 34 publications

(29 citation statements)

References 39 publications

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“…In Formula (12), w c is normalized to improve rendering efficiency, z c is computed according to Formula (4) to improve the Z-buffer's precision, and viewport size is replaced with image size. Finally, in the fragment shader, we use an off-screen texture to store visible fragments and a shader storage buffer object VisiblePrimitiveArray to record visible primitives' indexes at the corresponding subscripts (as shown in Figure 9).…”

Section: Ivm Generation In Fixed Graphics Pipelinementioning

confidence: 99%

“…Situations in which 3D visibility occurs include stereo matching [6], object reconstruction [7,8] and motion trajectory optimization [9][10][11]. With the development of multiview-based reconstruction [12], there has been an increasing focus on texture generation of 3D surfaces [13][14][15][16][17]. Visibility checking is highly important for the smoothness and verisimilitude of textured models.…”

Section: Introductionmentioning

confidence: 99%

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GPVC: Graphics Pipeline-Based Visibility Classification for Texture Reconstruction

2018

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The shadow-mapping and ray-tracing algorithms are the two popular approaches used in visibility handling for multi-view based texture reconstruction. Visibility testing based on the two algorithms needs a user-defined bias to reduce computation error. However, a constant bias does not work for every part of a geometry. Therefore, the accuracy of the two algorithms is limited. In this paper, we propose a high-precision graphics pipeline-based visibility classification (GPVC) method without introducing a bias. The method consists of two stages. In the first stage, a shader-based rendering is designed in the fixed graphics pipeline to generate initial visibility maps (IVMs). In the second stage, two algorithms, namely, lazy-projection coverage correction (LPCC) and hierarchical iterative vertex-edge-region sampling (HIVERS), are proposed to classify visible primitives into fully visible or partially visible primitives. The proposed method can be easily implemented in the graphics pipeline to achieve parallel acceleration. With respect to efficiency, the proposed method outperforms the bias-based methods. With respect to accuracy, the proposed method can theoretically reach a value of 100%. Compared with available libraries and software, the textured model based on our method is smoother with less distortion and dislocation.

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Section: Ivm Generation In Fixed Graphics Pipelinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GPVC: Graphics Pipeline-Based Visibility Classification for Texture Reconstruction

2018

Self Cite

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“…In contrast to ALS data, UAV images can be easily captured using a UAV equipped with cameras, which is cheaper and more convenient for regular inspections [3]. The extrinsic and intrinsic parameters of cameras can be determined using the method of structure from motion (SfM) with the auxiliary information GNSS/IMU (Global Navigation Satellite System/Inertial Measurement Unit) [4][5][6]. The dense point clouds of high-voltage transmission corridors can be generated by the multi-view stereo (MVS) method.…”

Section: Introductionmentioning

confidence: 99%

A Model-Driven Method for Pylon Reconstruction from Oblique UAV Images

Huang

Jiang

2020

Sensors

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Pylons play an important role in the safe operation of power transmission grids. Directly reconstructing pylons from UAV images is still a great challenge due to problems of weak texture, hollow-carved structure, and self-occlusion. This paper presents an automatic model-driven method for pylon reconstruction from oblique UAV images. The pylons are reconstructed with the aid of the 3D parametric model library, which is represented by connected key points based on symmetry and coplanarity. First, an efficient pylon detection method is applied to detect the pylons in the proposed region, which are obtained by clustering the line segment intersection points. Second, the pylon model library is designed to assist in pylon reconstruction. In the predefined pylon model library, a pylon is divided into two parts: pylon body and pylon head. Before pylon reconstruction, the pylon type is identified by the inner distance shape context (IDSC) algorithm, which matches the shape contours of pylon extracted from UAV images and the projected pylon model. With the a priori shape and coplanar constraint, the line segments on pylon body are matched and the pylon body is modeled by fitting four principle legs and four side planes. Then a Markov Chain Monte Carlo (MCMC) sampler is used to estimate the parameters of the pylon head by computing the maximum probability between the projected model and the extracted line segments in images. Experimental results on several UAV image datasets show that the proposed method is a feasible way of automatically reconstructing the pylon.

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“…Detailed three-dimensional (3D) reconstruction of ancient bridges provides a new way to solve this problem. This kind of work can be achieved using either range-based techniques, such as terrestrial laser scanners (TLS), or image-based techniques, mainly photogrammetry including structure-from-motion (SfM) [1,2]. Compared to the range-based approach, the image-based approach has several advantages, including easy acquisition and low costs.…”

Section: Introductionmentioning

confidence: 99%

“…The quality of relative orientation will directly influence the success rate of extracting sparse corresponding points and the further rectification of images [19]. The second point is the dense matching technique [2,20], which is a critical step to generate dense point clouds from the orientated images. Only when problems involving these two points are solved can qualified point clouds be obtained with high accuracy for further applications.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Reconstruction of Structural Surface Model of Heritage Bridges Using UAV-Based Photogrammetric Point Clouds

et al. 2019

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Three-dimensional (3D) digital technology is essential to the maintenance and monitoring of cultural heritage sites. In the field of bridge engineering, 3D models generated from point clouds of existing bridges is drawing increasing attention. Currently, the widespread use of the unmanned aerial vehicle (UAV) provides a practical solution for generating 3D point clouds as well as models, which can drastically reduce the manual effort and cost involved. In this study, we present a semi-automated framework for generating structural surface models of heritage bridges. To be specific, we propose to tackle this challenge via a novel top-down method for segmenting main bridge components, combined with rule-based classification, to produce labeled 3D models from UAV photogrammetric point clouds. The point clouds of the heritage bridge are generated from the captured UAV images through the structure-from-motion workflow. A segmentation method is developed based on the supervoxel structure and global graph optimization, which can effectively separate bridge components based on geometric features. Then, recognition by the use of a classification tree and bridge geometry is utilized to recognize different structural elements from the obtained segments. Finally, surface modeling is conducted to generate surface models of the recognized elements. Experiments using two bridges in China demonstrate the potential of the presented structural model reconstruction method using UAV photogrammetry and point cloud processing in 3D digital documentation of heritage bridges. By using given markers, the reconstruction error of point clouds can be as small as 0.4%. Moreover, the precision and recall of segmentation results using testing date are better than 0.8, and a recognition accuracy better than 0.8 is achieved.

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Efficient SfM for Oblique UAV Images: From Match Pair Selection to Geometrical Verification

Cited by 34 publications

References 39 publications

GPVC: Graphics Pipeline-Based Visibility Classification for Texture Reconstruction

GPVC: Graphics Pipeline-Based Visibility Classification for Texture Reconstruction

A Model-Driven Method for Pylon Reconstruction from Oblique UAV Images

Three-Dimensional Reconstruction of Structural Surface Model of Heritage Bridges Using UAV-Based Photogrammetric Point Clouds

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