Geometry　images　are　a　kind　of　completely　regular　remeshing　methods　for　mesh　representation.　Traditional　geometry　images　have　difficulties　in　achieving　optimal　reconstruction　errors　and　preserving　manually　selected　geometric　details,　due　to　the　limitations　of　parametrization　methods.　To　solve　two　issues,　we　propose　two　adaptive　geometry　images　for　remeshing　triangular　meshes.　The　first　scheme　produces　geometry　images　with　the　minimum　Hausdorff　error　by　finding　the　optimization　direction　for　sampling　points　based　on　the　Hausdorff　distance　between　the　original　mesh　and　the　reconstructed　mesh.　The　second　scheme　produces　geometry　images　with　higher　reconstruction　precision　over　themanually　selected　region-of-interest　of　the　inputmesh,　by　increasing　the　number　of　sampling　points　over　the　region-of-interest.　Experimental　results　show　that　both　schemes　give　promising　results　compared　with　traditional　parametrization-based　geometry　images.　Copyright　©　2017　Lina　Shi　et　al.