3D reconstruction of urban environments using in-vehicle fisheye camera

Ishii, Jumpei; Sakai, Shuji; Ito, Koichi; Aoki, Takafumi; Yanagi, Takura; Ando, Toshiyuki

doi:10.1109/icip.2013.6738442

Cited by 9 publications

(2 citation statements)

References 10 publications

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“…To obtain accurate and dense 3D points, the feature points were extracted from every video frames that contain many features and can be found at all frames (J. Ishii et al, 2013). To get more accurate matching points, the selection of key frames is required to improve the efficiency (C. Zhang et al, 2017).…”

Section: Extraction Of Point Cloudmentioning

confidence: 99%

Development of 3d City Model Using Videogrammetry Technique

Hairuddin

Azri

Ujang

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 3D city model is a representation of urban area in digital format that contains building and other information. The current approaches are using photogrammetry and laser scanning to develop 3D city model. However, these techniques are time consuming and quite costly. Besides that, laser scanning and photogrammetry need professional skills and expertise to handle hardware and tools. In this study, videogrammetry is proposed as a technique to develop 3D city model. This technique uses video frame sequences to generate point cloud. Videos are processed using EyesCloud3D by eCapture. EyesCloud3D allows user to upload raw data of video format to generate point clouds. There are five main phases in this study to generate 3D city model which are calibration, video recording, point cloud extraction, 3D modeling and 3D city model representation. In this study, 3D city model with Level of Detail 2 is produced. Simple query is performed from the database to retrieve the attributes of the 3D city model.

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Section: Extraction Of Point Cloudmentioning

confidence: 99%

Development of 3d City Model Using Videogrammetry Technique

Hairuddin

Azri

Ujang

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…In recent years, omnidirectinal vision sensors have been increasingly applied in intelligent transportation surveillance [1][2][3], robot navigation [4,5], medical endoscopy [6][7][8] and so on. Compared with the conventional pinhole camera model, the biggest advantage of the use of omnidirectinal vision sensor is that they have a field of view of 180° or even that exceeds 180°.…”

Section: Introductionmentioning

confidence: 99%

Estimation of fisheye camera external parameter based on second‐order cone programming

Zhu

Zhang

Zhou

et al. 2016

IET Computer Vision

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Although second‐order cone programming (SOCP) has been applied to optimise camera parameters in computer vision, it is occasionally been used to refine fisheye camera external parameters as well. This study presents a fisheye camera external parameter estimation based on SOCP in convex optimisation. The homography constraint between two spherical images are first exploited to derive an equation with respect to a given error threshold. Then, the fisheye camera external estimation is transformed into an SOCP optimisation problem through reformulating the parameter estimation equation. The SOCP method has been implemented in Matlab and the optimisation toolbox has been made publicly available. The fisheye camera external parameter optimisation method has been validated by some experiments with synthetic and real data. Comparison experiments between the proposed method and other methods in the literature are also carried out, and the results show that the SOCP method is better for the corrected images.

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Sparse Depth Calculation Using Real-Time Key-Point Detection and Structure from Motion for Advanced Driver Assist Systems

Prakash

Akhbari

et al. 2014

Advances in Visual Computing

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3D reconstruction of urban environments using in-vehicle fisheye camera

Cited by 9 publications

References 10 publications

Development of 3d City Model Using Videogrammetry Technique

Development of 3d City Model Using Videogrammetry Technique

Estimation of fisheye camera external parameter based on second‐order cone programming

Sparse Depth Calculation Using Real-Time Key-Point Detection and Structure from Motion for Advanced Driver Assist Systems

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