Processing, distribution, and rendering of massive point clouds


Processing, distribution and rendering of massive point clouds



The increasing performance and proliferation of digital photography and 3D scanning devices make it possible to acquire, at reasonable costs, very dense and accurate sampling of both geometric and optical surface properties of real objects. Point clouds are one of the most used data types to represent such models in fields like engineering, environmental sciences, or cultural heritage. They are naturally scalable as, the more samples the dataset has, the finer is the representation of the real object or scene. However, current point cloud datasets may become intractable on nowadays hardware, given that they can easily exceed the billions of samples. Managing such large datasets requires scalable techniques for the entire pipeline, from capture and editing to visualization.


CRS4 has created and developed over the years a suite of methods for supporting the management, distribution and rendering of massive 3D point clouds, such as those acquired by laser scanning or photogrammetric techniques. Our solutions include editable out-of-core multiresolution representations, data fusion techniques for merging color and photographic data, as well as optimized representations for compression and streaming. These tools have been used in industrial applications and licensed to geomatics companies. Moreover, they have been widely used in cultural heritage projects, such as Digital Mont’e Prama.

Innovative features

  • out-of-core management of editable point clouds;
  • fusion of photographic data by weighted blending;
  • complete solutions for scalable processing, remote distribution, and rendering of point cloud models on a variety of platforms.

Potential users

Researchers in visual computing, engineers active in industrial settings, experts working in cultural heritage.

Impact sectors

ICT - Cultural Heritage.

Other resources

  1. Digital Mont’e Prama
  2. Alberto Jaspe Villanueva. Scalable Exploration of 3D Massive Models. PhD thesis. PhD Programme in Information and Communications Technology, University of A Coruña, Spain, 2018.
  3. Ruggero Pintus and Enrico Gobbetti. A Fast and Robust Framework for Semi-Automatic and Automatic Registration of Photographs to 3D Geometry. ACM Journal on Computing and Cultural Heritage, 7(4): 23:1-23:23, February 2015.
  4. Fabio Bettio, Alberto Jaspe Villanueva, Emilio Merella, Fabio Marton, Enrico Gobbetti, and Ruggero Pintus. Mont'e Scan: Effective Shape and Color Digitization of Cluttered 3D Artworks. ACM Journal on Computing and Cultural Heritage, 8(1): 4:1-4:23, 2015.

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