3D　reconstruction　from　single　hand-drawn　sketches　can　be　considered　as　a　task　of　single-view　reconstruction,　which　faces　great　challenge　of　lifting　the　dimension　of　the　geometric　representation　of　objects.　Most　of　the　reconstruction　networks　are　based　on　deep　learning　technology　with　supervised　training　which　are　used　to　suffer　from　dataset　labeling,　while　self-supervised　sketch-based　3D　reconstruction　remains　challenging.　In　this　paper,　we　propose　a　self-supervised　3D　reconstruction　network　for　hand-drawn　sketch　(IASSReNet),　which　introduces　image　information　as　an　auxiliary　to　address　the　ambiguity　and　sparsity　of　sketch.　In　order　to　obtain　image　information,　an　image　generator　is　firstly　designed　to　provide　augmented　information　for　the　reconstruction　through　a　sketch　feature　enhancement　module.　To　integrate　the　information　from　sketch　and　image,　we　use　a　spatially　corresponding　feature　transfer　module　to　fuse　their　feature.　Finally,　silhouettes　are　obtained　from　the　predicted　3D　mesh,　and　similarity　constraints　are　applied　to　the　sketch　contour　to　perform　self-supervised　training　on　the　network.　Experimental　results　on　multiple　datasets　show　that　our　method　outperforms　other　unsupervised　methods　and　is　competitive　with　some　supervised　methods.　©　2023　SPIE.