Exclusive grouped spatial hashing

Duan, Weiwei; Luo, Jianxin; Ni, Guiqiang; Tang, Bin; Hu, Qi; Gao, Yi

doi:10.1016/j.cag.2017.08.012

Cited by 6 publications

(6 citation statements)

References 24 publications

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“…Surface rendering: Lefebvre and Hoppe [65] use perfect spatial hashing to render the color surfaces of 3D volumetric textures. Alcantara et al [3], [4] (open-addressing cuckoo hashing), Garcia et al [37] (open-addressing robin hood hashing), Nießner et al [83] (spatial hashing), and Duan et al [29] (spatial hashing) all perform real-time surface rendering and reconstruction of 3D volumes within voxelized grids. Bastos and Celes [9] use perfect hashing to perform isosurface rendering and morphing of adaptively sampled distance fields (ADFs).…”

Section: Hashing Applicationsmentioning

confidence: 99%

“…Duan et al [29] present an exclusive grouped spatial hashing (EGSH) scheme that is optimized to compactly represent multi-dimensional domains that contain repetitive data (e.g., duplicate RGB or density values). The goal of this approach is to identify all groups of points that share the same data values and then, for each group, compress its points into a single group-wide value, avoiding the unnecessary storage of duplicates, which are significantly prevalent in some domains.…”

Section: Spatial Hashingmentioning

confidence: 99%

“…Both bottlenecks render these tables infeasible for updates or reconstructions. From Table II, the HortonHash [16], PerfectHash [65], and EGSH [29] techniques are bandwidth-bound by the transfer of the hash table from CPU to GPU prior to querying. Additionally, MemcachedGPU [45] and StadiumHash [58] must service data transfers during querying, as hash table data resides on the CPU.…”

Section: Analysis and Future Workmentioning

confidence: 99%

See 2 more Smart Citations

Data-Parallel Hashing Techniques for GPU Architectures

Lessley

Childs

2020

IEEE Trans. Parallel Distrib. Syst.

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Hash tables are one of the most fundamental data structures for effectively storing and accessing sparse data, with widespread usage in domains ranging from computer graphics to machine learning. This study surveys the stateof-the-art research on data-parallel hashing techniques for emerging massively-parallel, many-core GPU architectures. Key factors affecting the performance of different hashing schemes are discovered and used to suggest best practices and pinpoint areas for further research.

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Section: Hashing Applicationsmentioning

confidence: 99%

Section: Spatial Hashingmentioning

confidence: 99%

Section: Analysis and Future Workmentioning

confidence: 99%

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Data-Parallel Hashing Techniques for GPU Architectures

Lessley

Childs

2020

IEEE Trans. Parallel Distrib. Syst.

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“…Planar compression and hash storage scheme: The voxel grid was set as the unit to organize by the hashing storing scheme, which contains the index of all voxels. The spatial hash [57][58][59][60][61][62][63] data structure dealt with the voxels of observed space by marking them in the hash table with a pointer stored in it. Additionally, structured data can be read and written effectively, allowing for faster calculation of features [64].…”

Section: Preprocessingmentioning

confidence: 99%

Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds

et al. 2019

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Electric power transmission and maintenance is essential for the power industry. This paper proposes a method for the efficient extraction and classification of three-dimensional (3D) targets of electric power transmission facilities based on regularized grid characteristics computed from point cloud data acquired by unmanned aerial vehicles (UAVs). First, a special hashing matrix was constructed to store the point cloud after noise removal by a statistical method, which calculated the local distribution characteristics of the points within each sparse grid. Secondly, power lines were extracted by neighboring grids’ height similarity estimation and linear feature clustering. Thirdly, by analyzing features of the grid in the horizontal and vertical directions, the transmission towers in candidate tower areas were identified. The pylon center was then determined by a vertical slicing analysis. Finally, optimization was carried out, considering the topological relationship between the line segments and pylons to refine the extraction. Experimental results showed that the proposed method was able to efficiently obtain accurate coordinates of pylon and attachments in the massive point data and to produce a reliable segmentation with an overall precision of 97%. The optimized algorithm was capable of eliminating interference from isolated tall trees and communication signal poles. The 3D geo-information of high-voltage (HV) power lines, pylons, conductors thus extracted, and of further reconstructed 3D models can provide valuable foundations for UAV remote-sensing inspection and corridor safety maintenance.

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“…They produced a sparse encoding in the form of a hierarchy and obtained high compression rates [10]. Duan et al presented a novel hashing scheme to compress highly repetitive spatial data [11]. Bastos [12] used octree level and the node location index to achieve a random access.…”

Section: Related Workmentioning

confidence: 99%

A New Linkless Hierarchical Structure based on Perfect Hashing

Duan¹,

Luo²,

Ni³

et al. 2018

dtcse

Self Cite

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We explore using perfect spatial hashing to store octrees efficiently while needing no explicit parent-to-child pointers. Specifically, we design a new linkless hierarchical structureone that encodes the absolute locations of the subdivided nodes using specific tokens and packs these nodes into hash tables using perfect hashing. Whereas prior methods have overhead in space and time due to the unused links and chain of the indirection pointers, our linkless hierarchical structure has lower storage requirements and has an efficient random accessibility on the GPUs. The performance of our scheme is demonstrated on different 2D/3D datasets together with some classical algorithms.

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Exclusive grouped spatial hashing

Cited by 6 publications

References 24 publications

Data-Parallel Hashing Techniques for GPU Architectures

Data-Parallel Hashing Techniques for GPU Architectures

Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds

A New Linkless Hierarchical Structure based on Perfect Hashing

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