Automatic 3D Reconstruction of Indoor Manhattan World Scenes Using Kinect Depth Data

Wolters, Dominik

doi:10.1007/978-3-319-11752-2_59

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…When talking about indoor reconstructions, 3D room can be obtained either using Microsoft Kinect [23]- [25] or image [26]. To overcome the problem of visual attributes' limitations such as transparent and highly reflective objects such as windows or mirrors, Kim et al [27] proposed the combined usage of audiovisual data to complement the visual limitations.…”

Section: Modeling Environmentmentioning

confidence: 99%

Blending of three-dimensional geometric model shapes

Rahmat

et al. 2022

IJEECS

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Three-dimensional (3D) geometric model shapes blending method can create various in-between models from two inputs of models shapes. Though, many blended shapes are implausible due to different inputs of model type, inappropriate matching-parts, improper parts-segmentation, and non-tally number of segmentation parts. are crucial and should be taken into account. The objective of this paper is to study the strengths and weaknesses of some prominent shapes blending methods and the 3D reconstruction methods. An interpolated shape blending program using the Laplacian-based contraction and Slinky-based segmentation method is developed to illustrate the critical problems arise in the shape blending process. Output results are to be compared with some prominent existing methods and one will observe the potential research direction in the blending research work

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Section: Modeling Environmentmentioning

confidence: 99%

Blending of three-dimensional geometric model shapes

Rahmat

et al. 2022

IJEECS

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“…The first step is the detection of the main planes of the scene. We use an approach based on a method that we have presented for the automatic indoor reconstruction based on dense point clouds (see [Wol14]).…”

Section: Detection Of the Main Planesmentioning

confidence: 99%

Robust and Accurate Detection of Mid-level Primitives for 3D Reconstruction in Man-Made Environments

Wolters¹

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The detection of geometric primitives such as points, lines and arcs is a fundamental step in computer vision techniques like image analysis, pattern recognition and 3D scene reconstruction. In this thesis, we present a framework that enables a reliable detection of geometric primitives in images. The focus is on application in man-made environments, although the process is not limited to this. The method provides robust and subpixel accurate detection of points, lines and arcs, and builds up a graph describing the topological relationships between the detected features. The detection method works directly on distorted perspective and fisheye images. The additional recognition of repetitive structures in images ensures the unambiguity of the features in their local environment. We can show that our approach achieves a high localization accuracy comparable to the state-of-the-art methods and at the same time is more robust against disturbances caused by noise. In addition, our approach allows extracting more fine details in the images. The detection accuracy achieved on the real-world scenes is constantly above that achieved by the other methods. Furthermore, our process can reliably distinguish between line and arc segments. The additional topological information extracted by our method is largely consistent over several images of a scene and can therefore be a support for subsequent processing steps, such as matching and correspondence search. We show how the detection method can be integrated into a complete feature-based 3D reconstruction pipeline and present a novel reconstruction method that uses the topological relationships of the features to create a highly abstract but semantically rich 3D model of the reconstructed scenes, in which certain geometric structures can easily be detected.

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Topology-Based 3D Reconstruction of Mid-Level Primitives in Man-Made Environments

Wolters

Koch

2019

Lecture Notes in Computer Science

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Automatic 3D Reconstruction of Indoor Manhattan World Scenes Using Kinect Depth Data

Cited by 6 publications

References 9 publications

Blending of three-dimensional geometric model shapes

Blending of three-dimensional geometric model shapes

Robust and Accurate Detection of Mid-level Primitives for 3D Reconstruction in Man-Made Environments

Topology-Based 3D Reconstruction of Mid-Level Primitives in Man-Made Environments

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