This　paper　proposes　a　depth　map　estimation　method　based　on　Deformable　Convolutional　Neural　Networks　(DCNNs).　Traditional　single　image　depth　estimation　methods　often　struggle　to　accurately　capture　irregular　shapes　and　details　in　complex　scenes,　resulting　in　suboptimal　spatial　resolution　and　object　boundary　reconstruction.　To　address　these　challenges,　we　introduce　deformable　convolution　modules　that　enable　convolutional　kernels　to　adaptively　adjust　their　sampling　positions,　thereby　enhancing　the　model＇s　capability　to　handle　complex　geometric　structures　and　dynamic　deformations.　Deformable　convolution　features　high　adaptability,　superior　handling　of　complex　scenes,　and　enhanced　boundary　clarity.　The　position　offsets　of　convolutional　kernels　are　adaptively　adjusted　through　learning,　allowing　the　model　to　flexibly　handle　features　of　various　shapes　and　scales,　thus　excelling　in　capturing　irregular　shapes　and　complex　details.　In　scenes　with　abundant　details　and　deformations,　deformable　convolution　can　extract　features　more　accurately,　significantly　improving　depth　estimation　accuracy.　Additionally,　by　finely　adjusting　sampling　points,　deformable　convolution　effectively　reduces　blurring　and　distortion　at　depth　map　boundaries,　excelling　in　reconstructing　small　objects　and　complex　edges.　Experimental　results　demonstrate　that　the　proposed　method　significantly　outperforms　existing　approaches　in　terms　of　depth　estimation　accuracy　and　boundary　clarity,　particularly　excelling　in　complex　scenes　and　small　object　reconstruction.　Our　research　highlights　the　extensive　application　potential　of　deformable　convolution　in　depth　map　estimation,　providing　robust　support　for　future　computer　vision　tasks.　©　2025　SPIE.