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A Regularized Volumetric Fusion Framework for large-scale 3d Reconstruction

ISPRS JOURNAL OF PHOTOGRAMMETRY ANd REMOTE SENSING 2018年 141卷 124-136页

作者： Rajput, Asif Funk, Eugen Boerner, Anko Hellwich, Olaf Deutsch Zentrum Luft & Raumfahrt DLR Inst Opt Sensor Syst Dept Informat Proc Opt Syst Berlin Germany Tech Univ Berlin Comp Vis & Remote Sensing Berlin Germany

CModern computational resources combined with low-cost depth sensing systems have enabled mobile robots to reconstruct 3d models of surrounding environments in real-time. Unfortunately, low-cost depth sensors are prone to produce undesirable estimation noise in depth measurements which result in either depth outliers or introduce surface deformations in the reconstructed model. Conventional 3d fusion frameworks integrate multiple error-prone depth measurements over time to reduce noise effects, therefore additional constraints such as steady sensor movement and high frame-rates are required for high quality 3d models. In this paper we propose a generic 3d fusion framework with controlled regularization parameter which inherently reduces noise at the time of data fusion. This allows the proposed framework to generate high quality 3d models without enforcing additional constraints. Evaluation of the reconstructed 3d models shows that the proposed framework outperforms state of art techniques in terms of both absolute reconstruction error and processing time.

关键词： Mobile Robotics large scale automated 3d modelling 3d Fusion Efficient data Structures Volumetric Integration

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Boundless Reconstruction Using Regularized 3d Fusion 13th

Boundless Reconstruction Using Regularized 3D Fusion

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13th International Joint Conference on e-Business and Telecommunications (ICETE)

作者： Rajput, M. A. A. Funk, E. Boerner, A. Hellwich, O. German Aerosp Ctr Inst Opt Sensor Syst Dept Informat Proc Opt Syst Berlin Germany Tech Univ Berlin Comp Vis & Remote Sensing Berlin Germany

ISBN: (纸本)9783319678764;9783319678757

3d reconstruction from image based depth sensor is essential part of many offline or online robotic applications. Numerous techniques have been developed to integrate multiple depth maps to create 3d model of environment, however accuracy of the reconstructed 3d model exclusively depends upon the precision of depth sensing. Economical depth sensors such as Kinect and stereo camera sensors provide imprecise depth data which affect the integration process and produce unwanted noisy surfaces in 3d model. There exist several approaches which use image filtering based depth map denoising, however applying filtering directly on depth data can result in inconsistent and deformed 3d model. In this paper we investigate and extend a recursive variant of total variation based filtering to incorporate multi-view based depth images while applying implicit depth smoothing. Proposed framework uses sparse voxel representation to aid large scale 3d model reconstruction and is shown to reduce absolute surface error of final reconstructed 3d model by up to 77% in comparison with state of the art 3d fusion techniques.

关键词： large scale automated 3d modelling Mobile robotics Efficient data structures 3d database

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Recursive Total Variation Filtering Based 3d Fusion

Recursive Total Variation Filtering Based 3D Fusion

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13th International Joint Conference on e-Business and Telecommunications (ICETE)

ISBN: (纸本)9789897581960

3d reconstruction from mobile image sensors is crucial for many offline-inspection and online robotic application. While several techniques are known today to deliver high accuracy 3d models from images via offline-processing, 3d reconstruction in real-time remains a major goal still to achieve. This work focuses on incremental 3d modeling from error prone depth image data, since standard 3d fusion techniques are tailored on accurate depth data from active sensors such as the Kinect. Imprecise depth data is usually provided by stereo camera sensors or simultaneous localization and mapping (SLAM) techniques. This work proposes an incremental extension of the total variation (TV) filtering technique, which is shown to reduce the errors of the reconstructed 3d model by up to 77% compared to state of the art techniques.

关键词： large scale automated 3d modelling Mobile Robotics Efficient data Structures 3d database

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