Camera　calibration　is　a　necessary　step　to　extract　3D　information　from　2D　images.　Since　the　1D　object　is　easy　to　construct　and　without　self-occlusion,　the　1D　calibration　proposed　by　Zhang　has　received　many　attentions.　However,　the　progress　in　1D　calibration　mainly　focuses　on　reducing　restrictions　on　the　1D　object＇s　movements.　The　calibration　accuracy　still　demands　improvements.　In　this　paper,　the　computational　model　of　1D　calibration　is　reformulated,　noises　in　1D　calibration　are　analyzed　with　this　model,　and　an　heteroscedastic　error-in-variables　model-based　1D　calibration　algorithm　is　proposed.　In　comparison　with　exiting　algorithms,　the　proposed　algorithm　has　advantages　of　high　accuracy　with　a　small　number　of　measurements,　rapid　convergence　and　weak　insensitivity　to　initial　conditions.　Experiments　with　both　synthetic　and　real　image　data　validate　the　proposed　algorithm.　©　2011　IEEE.