Point　cloud　data　has　been　extensively　used　in　all　kinds　of　applications,　such　as　autonomous　driving　and　augmented　reality,　since　it　can　provide　detailed　and　realistic　depictions　of　3D　scenes　or　objects.　Meanwhile,　3D　point　clouds　generally　occupy　a　large　amounts　of　storage　space　that　is　a　big　burden　for　efficient　communication.　However,　it　is　difficult　to　efficiently　compress　such　sparse,　disordered,　nonuniform　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　auto-encoders　that　progressively　downsamps　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　the　decreasing　of　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　object　point　cloud　dataset,　and　achieves　a　better　point　cloud　reconstruction　quality.