A Regularized Volumetric Fusion Framework for Large-Scale 3D Reconstruction

Rajput, Asif; Funk, Eugen; Börner, Anko; Hellwich, Olaf

doi:10.1016/j.isprsjprs.2018.04.020

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…[8] Three-dimensional fusion framework with controlled regularization parameter which reduces noise at the time of data fusion for generating three-dimensional models. [9] The fusion of data from a one-dimensional laser device and a vision system based on depth estimation for pose estimation and reconstruction. [10] High-quality three-dimensional maps of interior spaces using a hierarchical approach with the fusion of vision and range data.…”

Section: Approach Referencementioning

confidence: 99%

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Metrological analysis of the three-dimensional reconstruction based on close-range photogrammetry and the fusion of long-wave infrared and visible-light images

Marcellino

Rosa

Pinto

2020

Meas. Sci. Technol.

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This work proposes evaluating statistically the metrological performance of three-dimensional reconstructions built with fused long-wavelength infrared (LWIR) and visible-light (VL) images. The image fusion procedure was essentially based on two-dimensional wavelet transform and two pixel-level fusion rules: the maximum intensity level, presented in a previous work of the authors, and a new fusion rule, which replaces the VL information with the LWIR information in the region of the measured object on the images. The reconstructions of a translucent cube were performed with a point triangulation-based procedure and its dimension measurements were employed as evaluation criteria. The results show that the fused images have more contrast but also more artifacts. The fusion procedures generated denser reconstructions with at least 34.83% more points. Considering the metrological result, reconstructions with only visible-light images resulted in maximal 89.31% less measurement bias but at least 47.25% more uncertainty than the fusion ones. The new fusion rule provided the best results, with more points in the dense cloud and lower uncertainty. The work is important to provide a metrologically viable alternative for three-dimensional reconstruction of objects in situations of low contrast or poor texture information in the visible spectrum, and in which no target can be applied to the inspected part.

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Section: Approach Referencementioning

confidence: 99%

“…Other applications fuse devices with data from coordinate measurement machines [7]. The fusion of multiple VL devices is also considered [8,9,17]. The fusion of infrared and VL devices is also a frequent topic in this sense [6,[14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Metrological analysis of the three-dimensional reconstruction based on close-range photogrammetry and the fusion of long-wave infrared and visible-light images

Marcellino

Rosa

Pinto

2020

Meas. Sci. Technol.

View full text Add to dashboard Cite

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“…Each interest voxel v ∈ G is processed with a function f (v) : R 3 → R 1 which transforms spatial information of the voxel into an expected signed distance function followed by weighted integration which accommodates incremental updates to overall reconstruction. Traditional 3D reconstruction frameworks employ weighted integration of each incremental update to exploit stochastic convergence property, however this exploitation depends greatly on the number of updates and Rajput et al [23] showed that sensors (such as passive depth and LiDAR sensors) with lower sensing frequency are prone to produce noisy surfaces.…”

Section: Regularized Volumetric 3d Fusionmentioning

confidence: 99%

Multi-Sensor Depth Fusion Framework for Real-Time 3D Reconstruction

et al. 2019

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For autonomous robots, 3D perception of environment is an essential tool, which can be used to achieve better navigation in an obstacle rich environment. This understanding requires a huge amount of computational resources; therefore, the real-time 3D reconstruction of surrounding environment has become a topic of interest for countless researchers in the recent past. Generally, for the outdoor 3D models, stereo cameras and laser depth measuring sensors are employed. The data collected through the laser ranging sensors is relatively accurate but sparse in nature. In this paper, we propose a novel mechanism for the incremental fusion of this sparse data to the dense but limited ranged data provided by the stereo cameras, to produce accurate dense depth maps in real-time over a resource limited mobile computing device. Evaluation of the proposed method shows that it outperforms the state-of-the-art reconstruction frameworks which only utilizes depth information from a single source.

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“…3D reconstruction of indoor structures in complex environments has become an essential task for virtual reality, Building Information Management (BIM), emergency management, and other geoinformation-based activities [1][2][3][4][5]. Since reconstructing structural, watertight models with regularity and topologic consistency requires specific expertise and manual effort, developing efficient, robust and automatic reconstruction algorithms have attracted significant attention in recent years [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Topologically Consistent Reconstruction for Complex Indoor Structures from Point Clouds

Shan

2021

Remote Sensing

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Indoor structures are composed of ceilings, walls and floors that need to be modeled for a variety of applications. This paper proposes an approach to reconstructing models of indoor structures in complex environments. First, semantic pre-processing, including segmentation and occlusion construction, is applied to segment the input point clouds to generate semantic patches of structural primitives with uniform density. Then, a primitives extraction method with detected boundary is introduced to approximate both the mathematical surface and the boundary of the patches. Finally, a constraint-based model reconstruction is applied to achieve the final topologically consistent structural model. Under this framework, both the geometric and structural constraints are considered in a holistic manner to assure topologic regularity. Experiments were carried out with both synthetic and real-world datasets. The accuracy of the proposed method achieved an overall reconstruction quality of approximately 4.60 cm of root mean square error (RMSE) and 94.10% Intersection over Union (IoU) of the input point cloud. The development can be applied for structural reconstruction of various complex indoor environments.

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A Regularized Volumetric Fusion Framework for Large-Scale 3D Reconstruction

Cited by 8 publications

References 14 publications

Metrological analysis of the three-dimensional reconstruction based on close-range photogrammetry and the fusion of long-wave infrared and visible-light images

Metrological analysis of the three-dimensional reconstruction based on close-range photogrammetry and the fusion of long-wave infrared and visible-light images

Multi-Sensor Depth Fusion Framework for Real-Time 3D Reconstruction

Topologically Consistent Reconstruction for Complex Indoor Structures from Point Clouds

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