Learning-based　point　cloud　compression　has　achieved　great　success　in　Rate-Distortion　(RD)　efficiency.　Existing　methods　usually　utilize　Variational　AutoEncoder　(VAE)　network,　which　might　lead　to　poor　detail　reconstruction　and　high　computational　complexity.　To　address　these　issues,　we　propose　a　Scale-adaptive　Asymmetric　Sparse　Variational　AutoEncoder　(SAS-VAE)　in　this　work.　First,　we　develop　an　Asymmetric　Multiscale　Sparse　Convolution　(AMSC),　which　exploits　multi-resolution　branches　to　aggregate　multiscale　features　at　encoder,　and　excludes　symmetric　feature　fusion　branches　to　control　the　model　complexity　at　decoder.　Second,　we　design　a　Scale　Adaptive　Feature　Refinement　Structure　(SAFRS)　to　adaptively　adjust　the　number　of　Feature　Refinement　Modules　(FRMs),　thereby　improving　RD　performance　with　an　acceptable　computational　overhead.　Third,　we　implement　our　framework　with　AMSC　and　SAFRS,　and　train　it　with　an　RD　loss　based　on　Fine-grained　Weighted　Binary　Cross-Entropy　(FWBCE)　function.　Experimental　results　on　8iVFB,　Owlii,　and　MVUV　datasets　show　that　our　method　outperforms　several　popular　methods,　with　a　90.0%　time　reduction　and　a　51.8%　BD-BR　saving　compared　with　V-PCC.　The　code　will　be　available　soon　at　https://github.com/fancj2017/SAS-VAE.