Perfect Hashing Structures for Parallel Similarity Searches

Tran, Tuan Hiep; Giraud, Mathieu; Varré, Jean‐Stéphane

doi:10.1109/ipdpsw.2015.105

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…Although data integration for some voxel blocks (and re-integration [7]) might, hence, be staggered to a few subsequent frames, model consistency is still ensured by the high frame rate fusion. To achieve a more reliable GPU hashing, perfect hashing approaches [3,28,49] have been proposed that aim at collision-free hashing, but are hardly applicable for online reconstruction. In the context of collision handling, mini-mizing the maximum age of the hash map, i.e.…”

Section: Hashingmentioning

confidence: 99%

SLAMCast: Large-Scale, Real-Time 3D Reconstruction and Streaming for Immersive Multi-Client Live Telepresence

Stotko

Krumpen

Hullin

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Illustration of our novel multi-client live telepresence framework for remote collaboration: RGB-D data captured with consumergrade cameras represent the input to our real-time large-scale reconstruction technique that is based on a novel thread-safe GPU hash map data structure. Efficient data streaming is achieved by transmitting a novel compact representation of the reconstructed model in terms of Marching Cubes indices. Multi-client live telepresence is achieved by the server's independent handling of client requests.Abstract-Real-time 3D scene reconstruction from RGB-D sensor data, as well as the exploration of such data in VR/AR settings, has seen tremendous progress in recent years. The combination of both these components into telepresence systems, however, comes with significant technical challenges. All approaches proposed so far are extremely demanding on input and output devices, compute resources and transmission bandwidth, and they do not reach the level of immediacy required for applications such as remote collaboration. Here, we introduce what we believe is the first practical client-server system for real-time capture and many-user exploration of static 3D scenes. Our system is based on the observation that interactive frame rates are sufficient for capturing and reconstruction, and real-time performance is only required on the client site to achieve lag-free view updates when rendering the 3D model. Starting from this insight, we extend previous voxel block hashing frameworks by introducing a novel thread-safe GPU hash map data structure that is robust under massively concurrent retrieval, insertion and removal of entries on a thread level. We further propose a novel transmission scheme for volume data that is specifically targeted to Marching Cubes geometry reconstruction and enables a 90% reduction in bandwidth between server and exploration clients. The resulting system poses very moderate requirements on network bandwidth, latency and client-side computation, which enables it to rely entirely on consumer-grade hardware, including mobile devices. We demonstrate that our technique achieves state-of-the-art representation accuracy while providing, for any number of clients, an immersive and fluid lag-free viewing experience even during network outages.

show abstract

Section: Hashingmentioning

confidence: 99%

SLAMCast: Large-Scale, Real-Time 3D Reconstruction and Streaming for Immersive Multi-Client Live Telepresence

Stotko

Krumpen

Hullin

et al. 2019

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

“…Although data integration for some voxel blocks (and re-integration [7]) might, hence, be staggered to a few subsequent frames, model consistency is still ensured by the high frame rate fusion. To achieve a more reliable GPU hashing, perfect hashing approaches [3,28,48] have been proposed that aim at collision-free hashing, but are hardly applicable for online reconstruction. In the context of collision handling, mini-mizing the maximum age of the hash map, i.e.…”

Section: Hashingmentioning

confidence: 99%