Nowadays,　researchers　have　developed　various　deep　neural　networks　for　processing　point　clouds　effectively.　Due　to　the　enormous　parameters　in　deep　learning-based　models,　a　lot　of　manual　efforts　have　to　be　invested　into　annotating　sufficient　training　samples.　To　mitigate　such　manual　efforts　of　annotating　samples　for　a　new　scanning　device,　this　letter　focuses　on　proposing　a　new　neural　network　to　achieve　domain　adaptation　in　3-D　object　classification.　Specifically,　to　minimize　the　data　discrepancy　of　intraclass　objects　in　different　domains,　an　Asymmetrical　Siamese　(AS)　module　is　designed　to　align　the　intraclass　features.　To　preserve　the　discriminative　information　for　distinguishing　interclass　objects　in　different　domains,　a　Conditional　Adversarial　(CA)　module　is　leveraged　to　consider　the　classification　information　conveyed　from　the　classifier.　To　verify　the　effectiveness　of　the　proposed　method　on　object　classification　in　heterogeneous　point　clouds,　evaluations　are　conducted　on　three　point　cloud　datasets,　which　are　collected　in　different　scenarios　by　different　laser　scanning　devices.　Furthermore,　the　comparative　experiments　also　demonstrate　the　superior　performance　of　the　proposed　method　on　the　classification　accuracy.