Existing　works　have　made　some　progress　in　point　cloud　registration,　but　most　of　them　measure　performance　only　on　point　cloud　pairs　with　high　overlap.　In　practical　applications,　it　is　often　difficult　to　ensure　that　the　collected　point　clouds　overlap　in　large　regions　due　to　problems　such　as　occlusion　and　noise.　Therefore,　a　good　low-overlap　point　cloud　registration　method　is　of　great　practical　significance.　However,　extracting　reliable　correspondences　from　point　clouds　has　always　been　a　challenging　task,　particularly　when　dealing　with　low-overlap　situation.　In　this　paper,　we　propose　a　novel　method　for　low-overlap　point　cloud　registration　via　efficient　correspondence　augmentation,　called　AugLPCR,　which　not　only　enhances　correspondences　with　high　confidence,　but　also　employs　confidence　weights　to　mitigate　the　impact　of　outliers.　After　the　augmentation,　the　correspondences　used　for　the　transformation　have　a　large　amount　of　inliers,　leading　to　improved　registration　performance.　Extensive　experiments　on　indoor　and　outdoor　datasets　demonstrate　that　the　proposed　AugLPCR　is　capable　of　maintaining　consistent　performance　and　achieve　results　comparable　to　or　better　than　the　state-of-the-art　methods.　Note　to　Practitioners-The　motivation　of　this　paper　is　to　address　the　problem　of　registering　two　low-overlap　point　clouds.　Mainstream　algorithms　for　point　cloud　registration　typically　assume　a　sufficient　overlap　between　point　clouds.　However,　in　practical　scenarios,　it　is　common　to　encounter　scans　with　inadequate　overlap.　These　conditions　often　hinder　the　extraction　of　reliable　correspondences.　This　paper　introduces　an　effective　method　for　augmenting　correspondences　to　address　the　problem　of　low　inlier　rates　within　predicted　correspondences.　While　augmenting　correspondences　with　high　confidence,　it　also　mitigates　the　influence　of　outliers　and　ambiguous　points.　Additionally,　traditional　approaches　often　divide　superpoint　regions　before　matching,　but　this　can　lead　to　the　elimination　of　points　in　overlapping　regions　alongside　outliers.　To　address　this　issue,　we　adjust　the　order　of　superpoint　matching　and　region　partitioning.　The　proposed　framework　can　be　easily　applied　to　other　correspondence-based　point　cloud　registration　models.