Connectivity-preserving geometry images
We propose connectivity-preserving geometry images (CGIMs), which map a triangular mesh onto a rectangular regular array of an image, such that the reconstructed mesh produces no sampling errors, but merely round-off errors over the coordinates of vertices. Using permutation techniques on vertices, CGIMs first obtain a V-matrix whose elements are vertices of the original mesh, which intrinsically preserves the vertex-set and connectivity of the original mesh, and then generate a CGIM array by transforming the Cartesian coordinates of corresponding vertices of the V-matrix into RGB values. Compared with traditional geometry images (GIMs), CGIMs achieve the minimum reconstruction error with a parametrization-free algorithm. We apply CGIMs to lossy compression of meshes. Experimental results show that while CGIMs produce a lower efficiency in both encoding and decoding time and larger resolutions than traditional GIMs, CGIMs perform better peak signal-to-noise ratios and preserve details better than GIMs especially with the multi-stage base color and index map scheme, because CGIMs treat details and non-details of meshes evenly as all elements of the V-matrix.
connectivity-preserving cgim gim remeshing mesh parametrization