Point　cloud　data　are　extensively　used　in　various　applications,　such　as　autonomous　driving　and　augmented　reality　since　it　can　provide　both　detailed　and　realistic　depictions　of　3D　scenes　or　objects.　Meanwhile,　3D　point　clouds　generally　occupy　a　large　amount　of　storage　space　that　is　a　big　burden　for　efficient　communication.　However,　it　is　difficult　to　efficiently　compress　such　sparse,　disordered,　non-uniform　and　high　dimensional　data.　Therefore,　this　work　proposes　a　novel　deep-learning　framework　for　point　cloud　geometric　compression　based　on　an　autoencoder　architecture.　Specifically,　a　multi-layer　residual　module　is　designed　on　a　sparse　convolution-based　autoencoders　that　progressively　down-samples　the　input　point　clouds　and　reconstructs　the　point　clouds　in　a　hierarchically　way.　It　effectively　constrains　the　accuracy　of　the　sampling　process　at　the　encoder　side,　which　significantly　preserves　the　feature　information　with　a　decrease　in　the　data　volume.　Compared　with　the　state-of-the-art　geometry-based　point　cloud　compression　(G-PCC)　schemes,　our　approach　obtains　more　than　70-90%　BD-Rate　gain　on　an　object　point　cloud　dataset　and　achieves　a　better　point　cloud　reconstruction　quality.　Additionally,　compared　to　the　state-of-the-art　PCGCv2,　we　achieve　an　average　gain　of　about　10%　in　BD-Rate.