Monocular 3D scene reconstruction at absolute scale

Wöhler, Christian; d’Angelo, Pablo; Krüger, Lars; Kuhl, Annika; Groß, Horst–Michael

doi:10.1016/j.isprsjprs.2009.03.004

Cited by 19 publications

(17 citation statements)

References 15 publications

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“…(4) yields d i from σ i . However, measuring σ i does not work well as expected on features other than those with sharp edges due to the blur texture ambiguity [8]. In our previous work [9], we found that one of the main causes was the difference of the contrast between the ROIs and demonstrated that this error could be expressed by the following equation:…”

Section: Depth From Defocusmentioning

confidence: 81%

“…where subscript i is used to denote the i-th feature in an image, I i is a small region of interest (ROI) around the feature, I i f is the ROI around the same feature when this feature is best focused, and * indicates the convolution operation [8]. γ is a camera-specific value [26].…”

Section: Depth From Defocusmentioning

confidence: 99%

“…It relies on the amount of defocus blur which is related to the distance to the scene [8]. In our previous work [9], we demonstrated that combination of DfD and image velocity in an extended Kalman filter (EKF) framework was able to estimate the metric scale of an environment accurately.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Eliminating Scale Drift in Monocular SLAM Using Depth From Defocus

Shiozaki

Dissanayake

2018

IEEE Robot. Autom. Lett.

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Abstract-This paper presents a novel approach to correct errors caused by accumulated scale drift in monocular SLAM. It is shown that the metric scale can be estimated using information gathered through monocular SLAM and image blur due to defocus. A nonlinear least squares optimization problem is formulated to integrate depth estimates from defocus to monocular SLAM. An algorithm to process the output keyframe and feature location estimates generated by a monocular SLAM algorithm to correct for scale drift at selected local regions of the environment is presented. The proposed algorithm is experimentally evaluated by processing the output of ORB-SLAM [1] to obtain accurate metric scale maps from a monocular camera without any prior knowledge about the scene.

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Section: Depth From Defocusmentioning

confidence: 81%

Section: Depth From Defocusmentioning

confidence: 99%

See 1 more Smart Citation

Eliminating Scale Drift in Monocular SLAM Using Depth From Defocus

Shiozaki

Dissanayake

2018

IEEE Robot. Autom. Lett.

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“…However, this configuration has limited freedom and is specially manufactured. The authors in [20] consider defocus in structure from motion, although, for sparse structure computation.…”

Section: Related Workmentioning

confidence: 99%

Depth Estimation and Inpainting with an Unconstrained Camera

Bhavsar

Rajagopalan

2010

Procedings of the British Machine Vision Conference 2010

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Unrestricted camera motion and the ability to operate over a range of lens parameters are often desirable when using an off-the-shelf camera. Variations in intrinsic and extrinsic parameters induce defocus and pixel motion, both of which relate to scene structure. We propose a depth estimation approach by elegantly coupling the motion and defocus cues. We further advocate a natural extension of our framework for inpainting both depth and image, using the motion cue. Unlike traditional inpainting, our approach also considers defocus blur. This ensures that the image inpainting is coherent with respect to defocus. We use the belief propagation method in our estimation approach, which also handles occlusions and uses the color image segmentation cue.

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“…In addition, traditional photogrammetry-based reconstruction often requires multiple views of objects and known camera parameters. The majority of vision-based 3D reconstruction has focused on binocular vision (Furukawa and Ponce, 2010) and other algorithms requiring multiple images, such as structure and motion estimation (Nistér, 2005) and estimating depth from defocus (Wöhler et al, 2009). …”

Section: Introductionmentioning

confidence: 99%

Reconstructing Three-Dimensional Specific Curve Building Models From a Single Perspective View Image

Chang

Tsai

2012

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

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ABSTRACT:This paper presents a systematic approach to reconstruct 3D models of complex buildings with specific curve structures from single perspective view images. The proposed method begins with the automatic extraction of straight feature lines and corner points. Three mutually orthogonal vanishing points are then calculated automatically and robustly for reconstructing three-dimensional regular (planar structure) models. For curved segments, they are extracted and processed separately. Three types of curved segments are considered in this study, including cylinder shapes, surfaces of revolution and free-form curve models. Cylinder shape structures can be defined directly by calculated length of major and minor axes. As for surfaces of revolution, the model can be reconstructed using a fitted curve contour line and determined symmetry axis. Free-form curve structures are reconstructed using measured boundary curves for the interpolation of curve surfaces. Regular and curved models are then merged based on their shared feature points. Finally, the level of detail of reconstructed models can be increased using rectified façade texture rendered from single-view images. The output imagery of a computer-simulated model and close-range photographs of real buildings are used in this study to test the performance of the developed algorithms. Quantitative evaluations of the results compared with ground-based surveying and visualized comparison with raw images indicate that the developed algorithms can successfully extract 3D information and reconstruct 3D specific curve building models.

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Monocular 3D scene reconstruction at absolute scale

Cited by 19 publications

References 15 publications

Eliminating Scale Drift in Monocular SLAM Using Depth From Defocus

Eliminating Scale Drift in Monocular SLAM Using Depth From Defocus

Depth Estimation and Inpainting with an Unconstrained Camera

Reconstructing Three-Dimensional Specific Curve Building Models From a Single Perspective View Image

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