The　comprehension　of　3D　semantic　scenes　holds　paramount　significance　in　autonomous　driving　and　robotics　technology.　Nevertheless,　the　simultaneous　achievement　of　real-time　processing　and　high　precision　in　complex,　expansive　outdoor　environments　poses　a　formidable　challenge.　In　response　to　this　challenge,　we　propose　a　novel　occupancy　network　named　RTONet,　which　is　built　on　a　teacher-student　model.　To　enhance　the　ability　of　the　network　to　recognize　various　objects,　the　decoder　incorporates　dilated　convolution　layers　with　different　receptive　fields　and　utilizes　a　multi-path　structure.　Furthermore,　we　develop　an　automatic　frame　selection　algorithm　to　augment　the　guidance　capability　of　the　teacher　network.　The　proposed　method　outperforms　the　existing　grid-based　approaches　in　semantic　completion　(mIoU),　and　achieves　the　state-of-the-art　performance　in　terms　of　real-time　inference　speed　while　exhibiting　competitive　performance　in　scene　completion　(IoU)　on　the　SemanticKITTI　benchmark.