Boundless Reconstruction Using Regularized 3D Fusion

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

doi:10.1007/978-3-319-67876-4_17

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…Initially, the scale of reconstruction supported by KinectFusion was bounded due to limited GPU resources. Authors in [17] extended the working of KinectFusion to accommodate large scale reconstruction. At present, various implementations of volumetric fusion [5,6,16,17] utilize modern CPU and GPU architecture to facilitate virtually boundless reconstruction with the help of hashed voxel grids.…”

Section: Figmentioning

confidence: 99%

“…Authors in [17] extended the working of KinectFusion to accommodate large scale reconstruction. At present, various implementations of volumetric fusion [5,6,16,17] utilize modern CPU and GPU architecture to facilitate virtually boundless reconstruction with the help of hashed voxel grids. Therefore, in the presented system, we have extended a regularized implementation of [7] that provides controlled regularization to facilitate the implicit representation smoothing.…”

Section: Figmentioning

confidence: 99%

See 1 more Smart Citation

A Versatile Decentralized 3D Volumetric Fusion for On-line Reconstruction

Rajput¹,

Hussain²,

Akhtar³

et al. 2020

Eng. Technol. Appl. Sci. Res.

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Advancement in depth-sensing technology has allowed mobile robots to visualize the surrounding environment in 3D models. Regardless of the sensing technology (i.e. active, passive, or laser-based), a complete system that integrates recent depth data in previous 3D models in real-time is done by employing Simultaneous Localization And Mapping (SLAM) algorithms followed by a 3D reconstruction engine. Unfortunately, both the SLAM algorithm and the 3D reconstruction engine are usually executed on a single computing device, making the whole system exceptionally costly and heavy and restricting the robot's mobility. This paper proposes a decentralized, modular reconstruction system capable of employing various sensors to facilitate online 3D reconstruction from a resource-limited mobile robot.

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Section: Figmentioning

confidence: 99%

Section: Figmentioning

confidence: 99%

A Versatile Decentralized 3D Volumetric Fusion for On-line Reconstruction

Rajput¹,

Hussain²,

Akhtar³

et al. 2020

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

show abstract

“…State-of-the-art 3D reconstruction techniques (such as [15], [16] and [17]) make use of the volumetric integration process for high quality 3D reconstruction. Unfortunately, the scale of reconstruction based on volumetric integration is bounded with memory constraints and noisy depth data, this problem is addressed by Rajput et al [18] in which a sparse voxelbased approach is employed to extend the 3D reconstruction in boundless fashion.…”

Section: Related Workmentioning

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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Boundless Reconstruction Using Regularized 3D Fusion

Cited by 2 publications

References 16 publications

A Versatile Decentralized 3D Volumetric Fusion for On-line Reconstruction

A Versatile Decentralized 3D Volumetric Fusion for On-line Reconstruction

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

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