Registration of RGB and Thermal Point Clouds Generated by Structure From Motion

Truong, Trong Phuc; Yamawaki, Masahiro; Mori, Shohei; Nozick, Vincent; Saitō, Hideo

doi:10.1109/iccvw.2017.57

Cited by 21 publications

(25 citation statements)

References 25 publications

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“…The results of the proposed method and [4] present the means of | | with 100 trials both before and after BA. In result by [21], we adopt s t computed by Equation (6) in the paper of [21] as the scale parameter s. As shown in Fig. 6, the | | by [21] and [4] are much larger than the means of | | by the proposed method throughout the whole range of baseline length.…”

Section: Comparison With the Existing Methodsmentioning

confidence: 98%

“…Fig. 6 shows the comparison of the accuracies of the scale parameters estimated by Algorithm (2) of the proposed method, [21] and [4]. The results of the proposed method and [4] present the means of | | with 100 trials both before and after BA.…”

Section: Comparison With the Existing Methodsmentioning

confidence: 99%

“…6. The mean relative errors | | acquired by Algorithm (2) of the proposed method (ours), [21] and [4]. Ours and [4] have randomness caused by RANSAC.…”

Section: Evaluation With a Real Scenementioning

confidence: 99%

“…Ham et al [8] developed a method to directly match feature points between RGB and FIR images, which works only in rich thermal-texture environments. Under similar conditions, the method proposed by Truong et al [21] performs SfM using each of RGB and FIR images independently, aligning the two sparse point clouds.…”

Section: Thermal 3d Reconstructionmentioning

confidence: 99%

“…Thus, in this paper, we compare the proposed scale estimation method with the ones of Truong et al [21] and by Clipp et al [4].…”

Section: Scale Estimation For Monocular Sfmmentioning

confidence: 99%

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Scale Estimation of Monocular SfM for a Multi-modal Stereo Camera

Sumikura

Satoh

Kawaguchi

et al. 2019

Computer Vision – ACCV 2018

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This paper proposes a novel method of estimating the absolute scale of monocular SfM for a multi-modal stereo camera. In the fields of computer vision and robotics, scale estimation for monocular SfM has been widely investigated in order to simplify systems. This paper addresses the scale estimation problem for a stereo camera system in which two cameras capture different spectral images (e.g., RGB and FIR), whose feature points are difficult to directly match using descriptors. Furthermore, the number of matching points between FIR images can be comparatively small, owing to the low resolution and lack of thermal scene texture. To cope with these difficulties, the proposed method estimates the scale parameter using batch optimization, based on the epipolar constraint of a small number of feature correspondences between the invisible light images. The accuracy and numerical stability of the proposed method are verified by synthetic and real image experiments.

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Section: Comparison With the Existing Methodsmentioning

confidence: 98%

Section: Comparison With the Existing Methodsmentioning

confidence: 99%

“…6. The mean relative errors | | acquired by Algorithm (2) of the proposed method (ours), [21] and [4]. Ours and [4] have randomness caused by RANSAC.…”

Section: Evaluation With a Real Scenementioning

confidence: 99%

Section: Thermal 3d Reconstructionmentioning

confidence: 99%

“…Thus, in this paper, we compare the proposed scale estimation method with the ones of Truong et al [21] and by Clipp et al [4].…”

Section: Scale Estimation For Monocular Sfmmentioning

confidence: 99%

See 3 more Smart Citations

Scale Estimation of Monocular SfM for a Multi-modal Stereo Camera

Sumikura

Satoh

Kawaguchi

et al. 2019

Computer Vision – ACCV 2018

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A Hand-Held Sensor System for Exploration and Thermal Mapping of Volcanic Fumarole Fields

Irmisch

Ernst

Baumbach

et al. 2021

Communications in Computer and Information Science

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Research from the field of planetary- and geoscience require investigation of geological features in harsh environments. Sampled data needs to be precisely localized spatially and in time, and to be prepared appropriately for further inspection and evaluation. In the case of thermal image data, the computer vision community has made enormous progress in the past years to combine thermal with spatial information in form of thermal 3D models. In this paper, we propose to use a camera-based hand-held sensor system to capture and georeference thermal images in rugged terrain and to prepare the data for further investigations by visualizing the thermal data in 3D. We use a global localization solution to gain fast 3D impressions of the environment and further Structure from Motion to generate detailed mid-scale models of selected interesting structures. We demonstrate our application based on a challenging dataset that we acquired in the active fumarole fields of Vulcano, Sicily.

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