Automatic, accurate surface model inference for dental CAD/CAM

Abstract

Dental CAD/CAM offers the prospects of drastically reducing the time to provide service to patients, with no compromise on quality. Given the state-of-the-art in sensing, design, and machining, an attractive approach is to have a technician generate a restorative design in wax, which can then be milled by a machine in porcelain or titanium. The difficulty stems from the inherent outlier noise in the measurement phase. Traditional techniques remove noise at the cost of smoothing, degrading discontinuities such as anatomical lines which require accuracy up to 5 to 10 Μm to avoid artifacts. This paper presents an efficient method for the automatic and accurate data validation and 3-D shape inference from noisy digital dental measurements. The input consists of 3-D points with spurious samples, as obtained from a variety of sources such as a laser scanner or a stylus probe. The system produces faithful smooth surface approximations while preserving critical curve features such as grooves and preparation lines. To this end, we introduce the Tensor Voting technique, which efficiently ignores noise, infers smooth structures, and preserves underlying discontinuities. This method is non-iterative, does not require initial guess, and degrades gracefully with spurious noise, missing and erroneous data. We show results on real and complex data.

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