Error Analysis of Stereo Calibration and Reconstruction

Bier, Agnieszka; Luchowski, Leszek

doi:10.1007/978-3-642-01811-4_21

Cited by 10 publications

(2 citation statements)

References 6 publications

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“…The sensitivity of the triangulation approach is a relevant issue in investigation on 3D reconstruction [9]. Measurements of differences in the reconstruction position by rotation of one out of two cameras at a few degrees in the operating room may cause a significant difference in reconstruction position of the liver surface (Photo 3).…”

Section: Discussionmentioning

confidence: 99%

In vitro evaluation of stereoscopic liver surface reconstruction.

Spińczyk

Karwan²,

Zylkowski³

et al. 2013

Videosurgery and Other Miniinvasive Techniques

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IntroductionTracking abdominal motion of organs is an important factor in image-guided navigation systems. The paper presents the evaluation methodology of a practical approach to measure liver motion, both respiratory and laparoscopic, with a tool guided in the operating room. AimEvaluation of the methodology of a practical approach to measure liver motion, both respiratory and laparoscopic, with a tool guided in the operating room. Material and methodsThe presented evaluation method is based on standard operating room equipment, i.e. laparoscopic cameras. We decided to use two rigid cameras to gain stereo in order to reconstruct characteristic points by triangulation. Our research aim was to survey the impact of three parameters on reconstruction accuracy: the number of calibration points, the imprecision of camera assembly, and the difference in resolution of images. ResultsThree calibration chessboard configurations were tested. The reconstructed landmark positions and residual mean square errors were presented in three phantom poses: the reference position, translated position and rotated position. ConclusionsThe presented approach is a development of the previous work. Our research proved the importance of a rigid stereo camera system and the use of high definition image resolution for both stages, namely calibration and reconstruction.

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Section: Discussionmentioning

confidence: 99%

In vitro evaluation of stereoscopic liver surface reconstruction.

Spińczyk

Karwan²,

Zylkowski³

et al. 2013

Videosurgery and Other Miniinvasive Techniques

View full text Add to dashboard Cite

show abstract

“…In [ 3 ], the authors focus on studying the relationship between reconstruction accuracy and calibration accuracy, discussing the main factors involved in reconstruction errors. In [ 4 ], the problem of the propagation of input data errors in the stereovision process and its influence on the quality of reconstructed 3D points is addressed. In this case, the authors focus on camera calibration and 3D reconstruction algorithms that employ methods based on singular value decomposition (SVD).…”

Section: Introductionmentioning

confidence: 99%

Calibration of a Stereoscopic Vision System in the Presence of Errors in Pitch Angle

Felipe

Sigut

Sánchez

2022

Sensors

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This paper proposes a novel method for the calibration of a stereo camera system used to reconstruct 3D scenes. An error in the pitch angle of the cameras causes the reconstructed scene to exhibit some distortion with respect to the real scene. To do the calibration procedure, whose purpose is to eliminate or at least minimize said distortion, machine learning techniques have been used, and more specifically, regression algorithms. These algorithms are trained with a large number of vectors of input features with their respective outputs, since, in view of the application of the procedure proposed, it is important that the training set be sufficiently representative of the variety that can occur in a real scene, which includes the different orientations that the pitch angle can take, the error in said angle and the effect that all this has on the reconstruction process. The most efficient regression algorithms for estimating the error in the pitch angle are derived from decision trees and certain neural network configurations. Once estimated, the error can be corrected, thus making the reconstructed scene appear more like the real one. Although the authors base their method on U-V disparity and employ this same technique to completely reconstruct the 3D scene, one of the most interesting features of the method proposed is that it can be applied regardless of the technique used to carry out said reconstruction.

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Commodity RGB-D Sensors: Data Acquisition

Zollhöfer

2019

Advances in Computer Vision and Pattern Recognition

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Over the past ten years we have seen a democratization of range sensing technology. While previously range sensors have been highly expensive and only accessible to a few domain experts, such sensors are nowadays ubiquitous and can even be found in the latest generation of mobile devices, e.g., current smartphones. This democratization of range sensing technology was started with the release of the Microsoft Kinect, and since then many different commodity range sensors followed its lead, such as the Primesense Carmine, Asus Xtion Pro, and the Structure Sensor from Occipital. The availability of cheap range sensing technology led to a big leap in research, especially in the context of more powerful static and dynamic reconstruction techniques, starting from 3D scanning applications, such as KinectFusion, to highly accurate face and body tracking approaches. In this chapter, we have a detailed look into the different types of existing range sensors. We discuss the two fundamental types of commodity range sensing techniques in detail, namely passive and active sensing, and we explore the principles these technologies are based on. Our focus is on modern active commodity range sensors based on time-of-flight and structured light. We conclude by discussing the noise characteristics, working ranges, and types of errors made by the different sensing modalities.

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Error Analysis of Stereo Calibration and Reconstruction

Cited by 10 publications

References 6 publications

In vitro evaluation of stereoscopic liver surface reconstruction.

In vitro evaluation of stereoscopic liver surface reconstruction.

Calibration of a Stereoscopic Vision System in the Presence of Errors in Pitch Angle

Commodity RGB-D Sensors: Data Acquisition

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