Visibility of disjoint polygons
Algorithmica
Worst-case optimal algorithms for constructing visibility polygons with holes
SCG '86 Proceedings of the second annual symposium on Computational geometry
The design and analysis of spatial data structures
The design and analysis of spatial data structures
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Efficient construction of visibility maps using approximate occlusion sweep
SCCG '02 Proceedings of the 18th spring conference on Computer graphics
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Computing the visibility graph of points within a polygon
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Visibility and its dynamics in a PDE based implicit framework
Journal of Computational Physics
Efficient computation of query point visibility in polygons with holes
SCG '05 Proceedings of the twenty-first annual symposium on Computational geometry
Computer-Aided Design
Continuous nearest neighbor search
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Visibility-polygon search and euclidean shortest paths
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
Continuous visible nearest neighbor queries
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Continuous obstructed nearest neighbor queries in spatial databases
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Incremental Evaluation of Visible Nearest Neighbor Queries
IEEE Transactions on Knowledge and Data Engineering
A motion-aware approach for efficient evaluation of continuous queries on 3D object databases
The VLDB Journal — The International Journal on Very Large Data Bases
3D object retrieval using an efficient and compact hybrid shape descriptor
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
Maximum visibility queries in spatial databases
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
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Recent advances in 3D modeling provide us with real 3D datasets to answer queries, such as ''What is the best position for a new billboard?'' and ''Which hotel room has the best view?'' in the presence of obstacles. These applications require measuring and differentiating the visibility of an object (target) from different viewpoints in a dataspace, e.g., a billboard may be seen from many points but is readable only from a few points closer to it. In this paper, we formulate the above problem of quantifying the visibility of (from) a target object from (of) the surrounding area with a visibility color map (VCM). A VCM is essentially defined as a surface color map of the space, where each viewpoint of the space is assigned a color value that denotes the visibility measure of the target from that viewpoint. Measuring the visibility of a target even from a single viewpoint is an expensive operation, as we need to consider factors such as distance, angle, and obstacles between the viewpoint and the target. Hence, a straightforward approach to construct the VCM that requires visibility computation for every viewpoint of the surrounding space of the target is prohibitively expensive in terms of both I/Os and computation, especially for a real dataset comprising thousands of obstacles. We propose an efficient approach to compute the VCM based on a key property of the human vision that eliminates the necessity for computing the visibility for a large number of viewpoints of the space. To further reduce the computational overhead, we propose two approximations; namely, minimum bounding rectangle and tangential approaches with guaranteed error bounds. Our extensive experiments demonstrate the effectiveness and efficiency of our solutions to construct the VCM for real 2D and 3D datasets.