Abstract
This study presents a new method for reconstructing an adaptive underlying surface, \(\tilde{\varvec{S}}\), from scanned data for styling design objects. \(\tilde{\varvec{S}}\) is usually generated by sweeping a curve that varies its shape gradually while being swept. However, when \(\tilde{\varvec{S}}\) is reconstructed from a segmented part of the scanned data, it is generally more difficult to control the gradual variation as the ratio of the segmented part to the area of \(\tilde{\varvec{S}}\) becomes smaller. Therefore, this study represents \(\tilde{\varvec{S}}\) by the sum of two surfaces, \(\tilde{\varvec{S}}={\varvec{S}}^{\mathrm{U}}+{\varvec{S}}^\Delta \). Here, an underlying surface \({\varvec{S}}^{\mathrm{U}}\) is generated by the standard sweep method and the difference surface \({\varvec{S}}^\Delta \) not only compensates for the error between \(\tilde{\varvec{S}}\) and the scanned data but also exhibits monotonous change in the curvatures. Consequently, the gradual change in a curve being swept is represented by \({\varvec{S}}^\Delta \), which does not encounter the aforementioned problem because it is intended to control the deviation from the “reference” \({\varvec{S}}^{\mathrm{U}}\) under the constraint of “curvature monotonicity.” The experimental results demonstrate the validity of surface reconstruction from real-world scanned data as well as an application of the proposed method.
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Tsuchie, S. Reconstruction of adaptive swept surfaces from scanned data for styling design. Vis Comput 38, 493–507 (2022). https://doi.org/10.1007/s00371-020-02030-0
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DOI: https://doi.org/10.1007/s00371-020-02030-0