Progressive 3D Model Acquisition with a Commodity Hand-Held Camera

Kang, Zhuoliang; Medioni, Gérard

doi:10.1109/wacv.2015.43

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Recent developments in mobile technology and the availability of 3D printers raised the need for 3D content even more and underlay the importance of 3D modeling. In a user-centric scenario, images are taken on the spot and processed by a 3D modeling pipeline on-the-fly [15,18]. Any feedback which gets available to the user helps to guide her acquisition and, even more importantly, assures that the 3D model represents the real-world object in the desired quality.…”

Section: Introductionmentioning

confidence: 99%

Progressive Structure from Motion

Locher

Havlena

Gool

2018

Computer Vision – ECCV 2018

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Structure from Motion or the sparse 3D reconstruction out of individual photos is a long studied topic in computer vision. Yet none of the existing reconstruction pipelines fully addresses a progressive scenario where images are only getting available during the reconstruction process and intermediate results are delivered to the user. Incremental pipelines are capable of growing a 3D model but often get stuck in local minima due to wrong (binding) decisions taken based on incomplete information. Global pipelines on the other hand need the access to the complete viewgraph and are not capable of delivering intermediate results. In this paper we propose a new reconstruction pipeline working in a progressive manner rather than in a batch processing scheme. The pipeline is able to recover from failed reconstructions in early stages, avoids to take binding decisions, delivers a progressive output and yet maintains the capabilities of existing pipelines. We demonstrate and evaluate our method on diverse challenging public and dedicated datasets including those with highly symmetric structures and compare to the state of the art.

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Section: Introductionmentioning

confidence: 99%

Progressive Structure from Motion

Locher

Havlena

Gool

2018

Computer Vision – ECCV 2018

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“…Later, an approach similar to MonoFusion was introduced in [67]. The system utilizes a sparse tracker for camera pose estimation combined with a dense patch-based tracker for dense reconstruction.…”

Section: Surface Reconstructionmentioning

confidence: 99%

“…MobileFusion [67] targets devices with limited resources like a smartphone by introducing the first pipeline for real-time surface reconstruction and camera tracking running on standard commodity mobile phones. They take advantage of a hybrid GPU/CPU pipeline that tracks the camera at 25 Hz.…”

Section: Surface Reconstructionmentioning

confidence: 99%

Robust and affordable localization and mapping for 3D reconstruction. Application to architecture and construction

Hoyo¹,

Javier²

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viii 1 introduction 1 1.1 The research framework 3 1.2 Objectives 7 1.3 Outline 8 2 background 10 2.1 Notation 10 2.2 Optimization framework 11 2.3 Simultaneous Localization and Mapping 19 2.4 ORB-SLAM 32 2.5 K-Nearest Neighbors 44 2.6 Surface reconstruction 46 2.7 Building Information Modelling 49 3 accurate and robust slam 60 3.1 Contributions 61 3.2 Methodology 74 3.3 Experimental results 92 3.4 Conclusions 113 4 building information modelling 119 4.1 Contributions 120 4.2 Methodology 138 4.3 Experimental results 141 4.4 Conclusions 148 5 conclusions 153 5.1 Discussion 154 5.2 Future work 156 i appendix a appendix 159 a.1 Homographies and fundamental matrices 159 a.2 Sequence characteristics 161 a.3 Percentage of Tracked Trajectory 164 a.

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Coverage optimization of visual sensor networks for observing 3-D objects: survey and comparison

Zhang

Chen

2019

Int J Intell Robot Appl

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Progressive 3D Model Acquisition with a Commodity Hand-Held Camera

Cited by 7 publications

References 19 publications

Progressive Structure from Motion

Progressive Structure from Motion

Robust and affordable localization and mapping for 3D reconstruction. Application to architecture and construction

Coverage optimization of visual sensor networks for observing 3-D objects: survey and comparison

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