Accurate　camera　calibration　is　a　pre-requirement　for　widespread　applications　of　the　multi-camera　system　in　cultural　and　creative　industry.　The　newly　emerging　one-dimensional　calibration　is　very　suitable　for　multi-camera　systems　since　one-dimensional　objects　are　out　of　self-occlusions.　However,　the　progress　in　one-dimensional　calibration　mainly　focuses　on　reducing　restrictions　on　the　movement　of　one-dimensional　objects,　and　the　calibration　accuracy　still　needs　to　be　improved.　In　this　paper,　an　accurate　algorithm　for　one-dimensional　calibration　based　on　the　heteroscedastic　error-in-variables　(HEIV)　model　is　proposed.　Firstly,　a　computational　model　of　one-dimensional　calibration　is　derived.　Secondly,　noises　in　one-dimensional　calibration　are　analyzed　in　detail　using　this　computational　model,　and　we　draw　a　conclusion　that　one-dimensional　calibration　can　be　seen　as　an　HEIV　problem.　Finally,　the　proposed　algorithm　is　elaborated.　This　algorithm　has　the　advantages　of　high　accuracy,　rapid　convergence　and　less　insensitivity　to　initial　conditions　over　the　exiting　algorithms.　Experiments　with　both　synthetic　and　real　image　data　validate　the　proposed　algorithm.　©　2014　Acta　Automatica　Sinica.　All　rights　reserved.