Real-time RGB-D mapping and 3-D modeling on the GPU using the random ball cover data structure

Neumann, Dominik; Lugauer, Felix; Bauer, Sebastian; Wasza, Jakob; Hornegger, Joachim

doi:10.1109/iccvw.2011.6130381

Cited by 27 publications

(19 citation statements)

References 15 publications

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“…Trading accuracy against runtime, we propose a modified approximate RBC variant that is optimized in terms of performance. Please note that this chapter is a substantial extension of previous work by the authors [30]. In particular, we further enhanced the GPU implementation and achieved significant speedups.…”

Section: Introductionmentioning

confidence: 56%

“…Optimizing this particular ICP stage is motivated by the fact that it is a major performance bottleneck -see Sect. 5.2 and [30].…”

Section: -D Nearest Neighbor Search Using Rbcmentioning

confidence: 99%

“…A single ICP iteration consists of three steps: (i) nearest neighbor search using RBC, (ii) transformation estimation and (iii) application of the transformation. With an increasing number of landmarks, the nearest neighbor search dominates the runtime considerably [30]. Hence, we have put emphasis on optimizing the RBC construction and query performance.…”

Section: Icp Using Rbcmentioning

confidence: 99%

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Real-Time RGB-D Mapping and 3-D Modeling on the GPU Using the Random Ball Cover

Bauer

Wasza

Lugauer

et al. 2013

Consumer Depth Cameras for Computer Vision

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In this chapter, we present a system for real-time point cloud mapping and scene reconstruction based on an efficient implementation of the iterative closest point (ICP) algorithm on the graphics processing unit (GPU). Compared to state-ofthe-art approaches that achieve real-time performance using projective data association schemes which operate on the 3-D scene geometry solely, our method allows to incorporate additional complementary information to guide the registration process. In this work, the ICP's nearest neighbor search evaluates both geometric and photometric information in a direct manner, achieving robust mappings in real-time. In order to overcome the performance bottleneck in nearest neighbor search space traversal, we exploit the inherent computation parallelism of GPUs. In particular, we have adapted the random ball cover (RBC) data structure and search algorithm, originally proposed for high-dimensional problems, to low-dimensional RGB-D data. The system is validated on scene and object reconstruction scenarios. Our implementation achieves frame-to-frame registration runtimes of less than 20 ms on an off-the-shelf consumer GPU.

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Section: Introductionmentioning

confidence: 56%

“…Optimizing this particular ICP stage is motivated by the fact that it is a major performance bottleneck -see Sect. 5.2 and [30].…”

Section: -D Nearest Neighbor Search Using Rbcmentioning

confidence: 99%

Section: Icp Using Rbcmentioning

confidence: 99%

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Real-Time RGB-D Mapping and 3-D Modeling on the GPU Using the Random Ball Cover

Bauer

Wasza

Lugauer

et al. 2013

Consumer Depth Cameras for Computer Vision

Self Cite

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show abstract

“…In big number data cases, the computation cost becomes large. By virtue of new search algorithms, in particular heuristic optimal methods, and the increase in computer speed achieved by using multi-core computer processor units (CPUs) and graphic computation units (GPUs) [12], it is possible to find reasonable solutions using global approaches for the registration problem. When the coarse transformation has been estimated, the ICP algorithm is an efficient tool for finding the fine transformation.…”

Section: Introductionmentioning

confidence: 99%

Global ray-casting range image registration

Tao

Bui

Hasegawa

2017

IPSJ T Comput Vis Appl

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This paper presents a novel method for pair-wise range image registration, a backbone task in world modeling, parts inspection and manufacture, object recognition, pose estimation, robotic navigation, and reverse engineering. The method finds the most suitable homogeneous transformation matrix between two constructed range images to create a more complete 3D view of a scene. The proposed solution integrates a ray casting-based fitness estimation with a global optimization method called improved self-adaptive differential evolution. This method eliminates the fine registration steps of the well-known iterative closest point (ICP) algorithm used in previously proposed methods, and thus, is the first direct global registration algorithm. With its parallel implementation potential, the ray casting-based algorithm speeds up the fitness calculation for the global optimization method, which effectively exploits the search space to find the best transformation solution. The integration was successfully implemented in a parallel paradigm on a multi-core computer processor to solve a simultaneous 3D localization problem. The fast, accurate, and robust results show that the proposed algorithm significantly improves on the registration problem over state-of-the-art algorithms.

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“…A lot of attention has been drawn to 3D reconstruction using dense RGB-D data (Izadi et al, 2011;Neumann et al, 2011;Dou et al, 2013). Such data are obtained by multiple acquisitions of the considered 3D scene under different viewpoints.…”

Section: Introductionmentioning

confidence: 99%

3D Reconstruction of Indoor Scenes using a Single RGB-D Image

Bokaris

Muselet

Trémeau

2017

Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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To cite this version:Panagiotis-Alexandros Bokaris, Damien Muselet, Alain Trémeau. 3D reconstruction of indoor scenes using a single RGB-D image. 12th International Conference on Computer Vision Theory and Applications (VISAPP 2017), Feb 2017 3D reconstruction of indoor scenes using a single RGB-D image Abstract:The three-dimensional reconstruction of a scene is essential for the interpretation of an environment. In this paper, a novel and robust method for the 3D reconstruction of an indoor scene using a single RGB-D image is proposed. First, the layout of the scene is identified and then, a new approach for isolating the objects in the scene is presented. Its fundamental idea is the segmentation of the whole image in planar surfaces and the merging of the ones that belong to the same object. Finally, a cuboid is fitted to each segmented object by a new RANSAC-based technique. The method is applied to various scenes and is able to provide a meaningful interpretation of these scenes even in cases with strong clutter and occlusion. In addition, a new ground truth dataset, on which the proposed method is further tested, was created. The results imply that the present work outperforms recent state-of-the-art approaches not only in accuracy but also in robustness and time complexity.

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Real-time RGB-D mapping and 3-D modeling on the GPU using the random ball cover data structure

Cited by 27 publications

References 15 publications

Real-Time RGB-D Mapping and 3-D Modeling on the GPU Using the Random Ball Cover

Real-Time RGB-D Mapping and 3-D Modeling on the GPU Using the Random Ball Cover

Global ray-casting range image registration

3D Reconstruction of Indoor Scenes using a Single RGB-D Image

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