Object　classification　model　used　in　point　clouds　requires　to　be　retrained　when　applying　on　heterogeneous　point　clouds　that　are　collected　from　different　kinds　of　laser　scanning　systems.　The　model　training　procedure　needs　supervised　information　from　the　corresponding　laser　scanning　systems.　However,　the　acquisition　of　supervised　information　is　labor-intensive　and　time-consuming.　This　letter　proposes　a　new　framework　to　exploit　objects　from　point　clouds　with　supervised　information　(source　domain)　to　directly　classify　objects　from　heterogeneous　point　clouds　with　no　supervised　information　(target　domain).　More　specifically,　to　alleviate　occlusions　and　point　density　variations,　intraclass　variations　in　object　classification,　the　proposed　framework　integrates　a　bag-of-words　model　to　effectively　describe　3-D　objects　of　point　clouds.　To　alleviate　the　difference　of　point　clouds　collected　from　various　devices,　a　joint　distribution　adaption　model　is　exploited　to　build　an　effective　feature　transformation　to　accomplish　the　adaption　in　the　source　and　target　domains.　The　proposed　framework　is　validated　on　data　sets,　which　contain　three　kinds　of　point　clouds.　Extensive　experiments　show　the　effectiveness　of　the　proposed　framework　on　classifying　3-D　objects　in　heterogeneous　point　clouds.