Efficient processing of large 3D point clouds

Elseberg, Jan; Borrmann, Dorit; Nüchter, Andreas

doi:10.1109/icat.2011.6102102

Cited by 62 publications

(45 citation statements)

References 13 publications

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“…More optimistic report can be found in Ref. 61 where the data set of 15896875 points is taken into the consideration, but an important observation is that there is an increase in the variance for the computing time for the NNS using the octree. Based on the state of the art it can be stated that there was no method for sufficient 3D space decomposition that allows the nearest neighbour to search in a bounded time what can be considered as a problem for real−time applications, therefore, the presented approach is promising.…”

Section: Discussionmentioning

confidence: 99%

“…There are two meaningful approaches for NN search using GPU. The first approach is using a regular grid decomposi− tion [61] and the second approach is using a kd−tree [60]. It can be observed that kd−tree decomposition is already used in several robotic applications what can give an impression that we do not need further research in such direction.…”

Section: Discussionmentioning

confidence: 99%

“…61 authors for an ease of imple− mentation and for a reduction of the number of floating point operations, employ 8 orders of traversal instead of all 48 possible sequences in which to traverse the children in their octree−based GPUICP. In Ref.…”

Section: Discussionmentioning

confidence: 99%

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On-line range images registration with GPGPU

Bedkowski

Naruniec

2013

Opto-Electronics Review

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This paper concerns implementation of algorithms in the two important aspects of modern 3D data processing: data registration and segmentation. Solution proposed for the first topic is based on the 3D space decomposition, while the latter on image processing and local neighbourhood search. Data processing is implemented by using NVIDIA compute unified device architecture (NIVIDIA CUDA) parallel computation. The result of the segmentation is a coloured map where different colours correspond to different objects, such as walls, floor and stairs. The research is related to the problem of collecting 3D data with a RGB-D camera mounted on a rotated head, to be used in mobile robot applications. Performance of the data registration algorithm is aimed for on-line processing. The iterative closest point (ICP) approach is chosen as a registration method. Computations are based on the parallel fast nearest neighbour search. This procedure decomposes 3D space into cubic buckets and, therefore, the time of the matching is deterministic. First technique of the data segmentation uses accele-rometers integrated with a RGB-D sensor to obtain rotation compensation and image processing method for defining pre-requisites of the known categories. The second technique uses the adapted nearest neighbour search procedure for obtaining normal vectors for each range point.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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On-line range images registration with GPGPU

Bedkowski

Naruniec

2013

Opto-Electronics Review

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“…As a data structure, octrees are applied in a variety of applications, most notably in the area of computer graphics for efficient rendering (Botsch et al, 2002;Surmann et al, 2003;Laine and Karras, 2010) and in the field of photogrammetry to store and address large point clouds (GirardeauMontaut et al, 2005;Elseberg et al, 2011). Another popular use case is the compression of static point clouds (Schnabel and Klein, 2006) or point cloud streams (Kammerl et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

et al. 2013

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Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.

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“…The out-of-core data structure introduced by [Elseberg et al, 2011] and published in the 3D Toolkit, overcomes this limitation and works with dynamically splitting the tree until a minimum number of points are exceeded or predefined maximum depth is reached. Thus, it adapts much better to nonuniformly distributed data, since no subdivision is performed at unoccupied parts of the dataset.…”

Section: Out-of-corementioning

confidence: 99%

Filtering of Point Clouds from Photogrammetric Surface Reconstruction

Wenzel

Rothermel

Fritsch

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The density and data volumes for recorded 3D surfaces increase steadily. In particular during photogrammetric surface reconstruction and laser scanning applications these volumes often exceed the limits of the available hardware and software. The large point clouds and meshes acquired during the projects contain billions of vertices and require scalable data handling frameworks for further processing. Beside the scalability to big data, these methods also should adapt to non-uniform data density and precision resulting from varying acquisition distances, as required for data from Photogrammetry and Laser Scanning. For this purpose, we present a framework called Pine Tree, which is based on an out-of-core octree. It enables fast local data queries, such as nearest neighbor queries for filtering, while dynamically storing and loading data from the hard disk. This way, large amounts of data can be processed on limited main memory. Within this paper, we describe the Pine Tree approach as well as its underlying methods. Furthermore, examples for a filtering task are shown, where overlapping point clouds are thinned out by preserving the locally densest point cloud only. By adding an optional redundancy constraint, point validation and outlier rejection can be applied.

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Efficient processing of large 3D point clouds

Cited by 62 publications

References 13 publications

On-line range images registration with GPGPU

On-line range images registration with GPGPU

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

Filtering of Point Clouds from Photogrammetric Surface Reconstruction

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