Recent 3D sketch tools produce networks of three-space curves that suggest the contours of shapes. The shapes may be non-manifold, closed three-dimensional, open two-dimensional, or mixed. Our video demonstrates a system that automatically generates intuitively appealing, piecewise-smooth surface patches from such a curve network, and an intelligent user interface for modifying the automatically chosen surface patches. Both parts of the system use a linear algebra representation of the set of surface patches to track the topology.

Abstract Recent 3D sketch tools produce networks of three‐space curves that suggest the contours of shapes. The shapes may be non‐manifold, closed three‐dimensional, open two‐dimensional, or mixed. We describe a system that automatically generates intuitively appealing piecewise‐smooth surfaces from such a curve network, and an intelligent user interface for modifying the automatically chosen surface patches. Both the automatic and the semi‐automatic parts of the system use a linear algebra representation of the set of surface patches to track the topology. On complicated inputs from ILoveSketch [ BBS08 ], our system allows the user to build the desired surface with just a few mouse‐clicks.

Abstract Optical triangulation laser scanners produce errors at surface discontinuities and sharp features. These systematic errors are anisotropic. We examine the causes of these errors theoretically, and we study the correlation of systematic error with edge size and orientation experimentally. We then present a novel processing method for removing systematic errors, by combining scans taken at several different orientations. We apply an anisotropic filter to the separate scans, and use it to weight the data in a final combination step. Unlike previous approaches, our method does not require access to the scanner's internal data or firmware. We demonstrate the technique on data from laser range scanners by two different manufacturers.

We demonstrate an efficient data-parallel algorithm for building large hash tables of millions of elements in real-time. We consider two parallel algorithms for the construction: a classical sparse perfect hashing approach, and cuckoo hashing, which packs elements densely by allowing an element to be stored in one of multiple possible locations. Our construction is a hybrid approach that uses both algorithms. We measure the construction time, access time, and memory usage of our implementations and demonstrate real-time performance on large datasets: for 5 million key-value pairs, we construct a hash table in 35.7 ms using 1.42 times as much memory as the input data itself, and we can access all the elements in that hash table in 15.3 ms. For comparison, sorting the same data requires 36.6 ms, but accessing all the elements via binary search requires 79.5 ms. Furthermore, we show how our hashing methods can be applied to two graphics applications: 3D surface intersection for moving data and geometric hashing for image matching.