It＇s　difficult　to　effectively　remove　redundancy　in　Li-DAR　point　clouds　due　to　their　extremely　sparse　and　nonuniform　distribution.　Taking　advantage　of　both　octree-based　methods　and　voxel-based　schemes,　we　propose　to　design　an　effective　temporal-spatial　context　to　compress　the　sequence　octree-structured　point　cloud　data　into　a　more　compact　bitstream.　In　this　paper,　we　first　build　a　temporal-spatial　multiscale　context　for　the　deep　learning　entropy　model.　It　further　utilize　the　correlation　of　sequential　point　cloud　data　from　both　the　spatial　domain　and　temporal　domain.　In　terms　of　spatial　context,　we　design　a　hierarchical　dependency　in　an　octree　to　encode　the　occupancy　information　of　each　non-leaf　octree　node　into　a　bitstream.　We　propose　to　further　group　the　nodes　according　to　their　octant　which　effectively　expands　the　context　receptive　field.　In　terms　of　temporal　context,　the　KNN　algorithm　is　applied　to　explore　the　most　relative　context　with　the　strongest　dependency　in　the　temporal　domain.　Finally,　we　design　a　voxel　re-localization　network　to　convert　the　discrete　voxels　into　refined　3D　points,　which　makes　up　for　the　coordinate　loss　in　the　process　of　generating　an　octree.　The　quantitative　evaluation　shows　that　our　method　outperforms　state-of-The-Art　baselines　with　saving　most　bitrate　on　KITTI　Odometry　dataset,　and　achieving　the　best　reconstreuction　benefit　by　the　designed　refinement　module.　©　2023　IEEE.