Due　to　the　cost　and　accuracy　of　current　point　cloud　sampling　equipment,　the　obtained　point　color　information　is　often　corrupted　by　various　noises.　Existing　point　cloud　denoising　algorithms　mainly　focus　on　smoothness　priors　and　convex　optimization.　Their　performances　highly　depend　on　model　parameters　whose　values　are　determined　manually　and　fixed　throughout　the　iterations.　In　this　paper,　we　propose　to　unfold　gradient　graph　regularization　with　deep　neural　networks　for　point　cloud　color　denoising.　It　improves　the　robustness　of　the　model　for　denoising　in　different　kinds　of　datasets　and　across　domains.　Specifically,　our　approach　first　uses　a　point　cloud　extraction　network　to　obtain　effective　features　for　gradient　computation.　Then,　we　construct　a　gradient　graph　Laplacian　regularization　(GGLR)　as　signal　smoothness　prior　to　point　cloud　restoration.　Finally,　we　introduce　shallow　neural　networks　for　model　parameter　estimation　to　unfold　GGLR.　The　proposed　point　cloud　denoising　framework　is　fully　differentiable　and　can　be　trained　end-to-end.　Experiments　show　that　the　proposed　algorithm　unfolding　outperforms　several　existing　point　cloud　color　denoising　techniques.