Significant　progress　has　been　achieved　in　deep　3D　reconstruction　from　a　single　frontal　view　with　the　aid　of　generative　models;　however,　the　unreliable　nature　of　generated　multi-views　continues　to　present　challenges　in　this　domain.　In　this　study,　we　propose　Recon3D,　a　novel　framework　for　3D　reconstruction.　Recon3D　exclusively　utilizes　a　generated　back　view,　which　can　be　obtained　more　reliably　through　generative　models　based　on　the　frontal　reference　image,　as　explicit　priors.　By　incorporating　these　priors　and　guidance　from　a　generative　model,　which　is　fine-tuned　with　Dreambooth　and　then　enhanced　with　ControlNet,　we　effectively　supervise　NeRF　rendering　in　the　latent　space.　Subsequently,　we　convert　the　NeRF　representation　into　an　explicit　point　cloud　and　further　optimize　the　explicit　representation　by　referencing　high-quality　textured　reference　views.　Extensive　experiments　demonstrate　that　our　method　achieves　state-of-the-art　performance　in　rendering　novel　views　with　superior　geometry　and　texture　quality.　©　2024　IEEE.