A Computer Vision Approach for Visualization from Sparse, Noisy 3-D Data

Chi-Keung Tang


Abstract

We are interested in feature extraction from volume data in terms of coherent surfaces and 3-D space curves. The input can be an inaccurate scalar, vector or tensor field, sampled densely or sparsely on a regular 3-D grid. Even if such data is obtained from state-of-the-art sensors or data sources, unavoidable outlier noise and missing data can make the traditional iso-surface techniques inappropriate. Here, we propose a unified computational scheme, Tensor Voting, for dealing with such real data. The representation scheme is one of a second-order symmetric tensor that encodes preferred direction, as well as orientation uncertainty information at discontinuities. Data communication is achieved by a novel voting process that simultaneously infers smooth structures, detect orientation discontinuities, and ignores outlier noise. This process produces a dense tensor map, which is further decomposed into a surface, a curve, and a junction map. A modified marching process is then used for coherent surface and curve extraction. In this talk, a live demo will be given, and other application domains such as flow visualization, vortex extraction, terrain, seismic data processing, and some medical imagery applications will be discussed.

On-line references

Integrated Surface Inference from Sparse Data Sets


Maintained by Alexandre R.J. FRANÇOIS