Camera　relocalization　is　a　challenging　task　to　estimate　camera　pose　within　a　known　scene,　with　wide　applications　in　the　fields　of　Virtual　Reality　(VR),　Augmented　Reality　(AR),　robotics,　and　etc.　Most　existing　learning-based　methods　invariably　utilize　all　the　information　within　an　image　for　pose　estimation.　Although　these　methods　have　demonstrated　leading　pose　accuracy　in　some　cases,　they　are　still　far　from　being　sufficient　to　handle　the　robustness　under　challenging　viewpoints　with　less　impacts　on　the　localization　accuracy　for　viewpoints　that　are　easier　to　localize.　In　this　paper,　we　propose　a　novel　two-branch　camera　pose　estimation　framework:　one　branch　utilizes　keypoint-guided　partial　scene　coordinate　regression,　while　the　other　employs　full　scene　coordinate　regression　to　assess　the　credibility　of　image　poses,　thereby　enabling　more　accurate　camera　localization.　In　particular,　we　devise　a　keypoint　selection　method　predicated　on　matching　rates　which　is　designed　to　measure　the　matching　quality　between　a　3D　keypoint　and　2D　keypoints　across　views.　With　these　selected　3D　keypoints,　we　can　generate　2D　supervision　mask　with　the　ground-truth　camera　pose　to　supervise　the　keypoint　prediction　from　the　keypoint　selection　network.　Meanwhile,　we　further　refine　the　2D　supervision　mask　through　the　optimization　with　reprojection　errors　on　the　scene　coordinate　network,　which　estimates　the　scene　coordinates　for　points　within　the　scene　that　truly　warrant　attention,　also　enhances　the　localization　performance.　We　also　introduce　a　gated　camera　pose　estimation　strategy　on　the　two-branch　pose　estimation　framework,　employing　an　updated　keypoint　selection　network　for　images　with　higher　credibility　and　a　more　robust　network　for　difficult　viewpoints.　By　adopting　an　effective　curriculum　learning　scheme,　we　achieve　higher　accuracy　within　a　training　span　of　just　20　minutes.　Our　method＇s　superior　performance　is　validated　through　rigorous　experimentation.　The　code　is　released　at　https://github.com/DUT-ICCD/KP-Guided-Reloc.　©　2024　ACM.