Guided　depth　map　super-resolution　(GDSR)　is　one　of　the　mainstream　methods　in　depth　map　super-resolution,　as　high-resolution　color　images　can　guide　the　reconstruction　of　the　depth　maps　and　are　often　easy　to　obtain.　However,　how　to　make　full　use　of　extracted　guidance　information　of　the　color　image　to　improve　the　depth　map　reconstruction　remains　a　challenging　problem.　In　this　paper,　we　first　design　a　multi-scale　feedback　module　(MF)　that　extracts　multi-scale　features　and　alleviates　the　information　loss　in　network　propagation.　We　further　propose　a　novel　multi-scale　feedback　network　(MSF-Net)　for　guided　depth　map　super-resolution,　which　can　better　extract　and　refine　the　features　by　sequentially　joining　MF　blocks.　Specifically,　our　MF　block　uses　parallel　sampling　layers　and　feedback　links　between　multiple　time　steps　to　better　learn　information　at　different　scales.　Moreover,　an　inter-scale　attention　module　(IA)　is　proposed　to　adaptively　select　and　fuse　important　features　at　different　scales.　Meanwhile,　depth　features　and　corresponding　color　features　are　interacted　using　cross-domain　attention　conciliation　module　(CAC)　after　each　MF　block.　We　evaluate　the　performance　of　our　proposed　method　on　both　synthetic　and　real　captured　datasets.　Extensive　experimental　results　validate　that　the　proposed　method　achieves　state-of-the-art　performance　in　both　objective　and　subjective　quality.