1,485 research outputs found
3DMADMAC|AUTOMATED: synergistic hardware and software solution for automated 3D digitization of cultural heritage objects
In this article a fully automated 3D shape measurement system and data processing algorithms are presented. Main purpose of this system is to automatically (without any user intervention) and rapidly (at least ten times faster than manual measurement) digitize whole object’s surface with some limitations to its properties: maximum measurement volume is described as a cylinder with 2,8m height and 0,6m radius, maximum object's weight is 2 tons. Measurement head is automatically calibrated by the system for chosen working volume (from 120mm x 80mm x 60mm and ends up to 1,2m x 0,8m x 0,6m). Positioning of measurement head in relation to measured object is realized by computer-controlled manipulator. The system is equipped with two independent collision detection modules to prevent damaging measured object with moving sensor’s head. Measurement process is divided into three steps. First step is used for locating any part of object’s surface in assumed measurement volume. Second step is related to calculation of "next best view" position of measurement head on the base of existing 3D scans. Finally small holes in measured 3D surface are detected and measured. All 3D data processing (filtering, ICP based fitting and final views integration) is performed automatically. Final 3D model is created on the base of user specified parameters like accuracy of surface representation and/or density of surface sampling. In the last section of the paper, exemplary measurement result of two objects: biscuit (from the collection of Museum Palace at Wilanów) and Roman votive altar (Lower Moesia, II-III AD) are presented
Point Cloud Framework for Rendering 3D Models Using Google Tango
This project seeks to demonstrate the feasibility of point cloud meshing for capturing and modeling three dimensional objects on consumer smart phones and tablets. Traditional methods of capturing objects require hundreds of images, are very slow and consume a large amount of cellular data for the average consumer. Software developers need a starting point for capturing and meshing point clouds to create 3D models as hardware manufacturers provide the tools to capture point cloud data. The project uses Googles Tango computer vision library for Android to capture point clouds on devices with depth-sensing hardware. The point clouds are combined and meshed as models for use in 3D rendering projects. We expect our results to be embraced by the Android market because capturing point clouds is fast and does not carry a large data footprint
Aggressive saliency-aware point cloud compression
The increasing demand for accurate representations of 3D scenes, combined
with immersive technologies has led point clouds to extensive popularity.
However, quality point clouds require a large amount of data and therefore the
need for compression methods is imperative. In this paper, we present a novel,
geometry-based, end-to-end compression scheme, that combines information on the
geometrical features of the point cloud and the user's position, achieving
remarkable results for aggressive compression schemes demanding very small bit
rates. After separating visible and non-visible points, four saliency maps are
calculated, utilizing the point cloud's geometry and distance from the user,
the visibility information, and the user's focus point. A combination of these
maps results in a final saliency map, indicating the overall significance of
each point and therefore quantizing different regions with a different number
of bits during the encoding process. The decoder reconstructs the point cloud
making use of delta coordinates and solving a sparse linear system. Evaluation
studies and comparisons with the geometry-based point cloud compression (G-PCC)
algorithm by the Moving Picture Experts Group (MPEG), carried out for a variety
of point clouds, demonstrate that the proposed method achieves significantly
better results for small bit rates
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