Three-dimensional　(3D)　point　cloud　data　have　been　widely　used　in　many　fields,　such　as　autonomous　driving,　robotics,　and　high-precision　mapping.　At　present,　the　state-of-the-art　deep　learning-based　methods　for　3D　point　cloud　processing　are　mainly　supervised　learning　methods.　The　performance　of　these　methods　depends　heavily　on　large-scale,　high-quality　annotated　datasets.　However,　annotating　a　large-scale,　high-quality,　category-diverse,　and　scenario-rich　dataset　is　time-consuming　and　labor-intensive.　In　particular,　obtaining　sufficiently　large　numbers　of　samples　for　model　optimization　is　also　quite　difficult　in　some　special　cases.　In　addition,　3D　point　cloud　processing　models　trained　on　a　single　device　in　a　special　environment　are　difficult　to　generalize　to　different　devices　and　environments.　Their　generalizability　to　various　devices　and　environments　is　limited.　Thus,　how　to　reduce　dependencies　on　high-quality　annotated　3D　point　cloud　datasets　and　how　to　improve　the　generalizability　of　current　point　cloud　processing　models　are　important　research　topics.　In　recent　years,　various　kinds　of　impressive　and　elaborate　technologies,　such　as　meta-learning,　few-shot　learning,　transfer　learning,　self-supervised　learning,　semisupervised　learning,　and　weakly　supervised　learning,　have　been　proposed　to　solve　this　problem.　As　an　important　research　branch　of　transfer　learning,　domain　adaptive　learning　aims　to　eliminate　differences　in　feature　distributions　across　domains　and　promote　the　generalization　ability　of　deep　learning　models,　thereby　providing　a　novel　solution　to　address　this　problem　effectively.　The　academic　community　has　conducted　preliminary　research　on　domain　adaptive　learning　for　point　cloud　processing.　However,　the　domain　adaptive　learning　field　for　point　clouds　still　requires　in-depth　and　effective　exploration.　Consequently,　this　study　systematically　summarizes　and　classifies　recent　3D　point　cloud　domain　adaptive　learning　methods　into　five　categories:　adversarial　learning,　cross-modal　learning,　pseudo-label　learning,　data　alignment,　and　other　kinds　of　methods.　First,　we　present　the　mathematical　definition　of　the　domain　adaptive　learning　task　and　depict　the　chronological　overview　of　the　development　of　different　domain　adaptive　learning　methods　to　provide　readers　with　a　clear　understanding.　Second,　we　present　the　general　solution　for　each　category　of　domain　adaptive　learning　methods　and　summarize　the　advantages　and　disadvantages　of　the　current　methods　for　each　category.　Third,　we　compare　the　performance　of　current　methods　on　three-point　cloud　processing　tasks,　including　3D　shape　classification,　3D　object　detection,　and　3D　semantic　segmentation.　For　each　task,　we　also　summarize　the　commonly　used　datasets　and　evaluation　metrics　for　an　intuitional　comparison.　Finally,　we　conclude　the　advantages　and　disadvantages　of　these　five　categories　of　methods　and　discuss　future　research　directions　about　the　3D　point　cloud　domain　adaptive　learning.　©　2024　Science　Press.　All　rights　reserved.