In　recent　years,addressing　ill-posed　problems　by　leveraging　prior　knowledge　contained　in　databases　on　learning　techniques　has　gained　much　attention.In　this　paper,we　focus　on　complete　three-dimensional(3D)point　cloud　reconstruction　based　on　a　single　red-green-blue(RGB)image,a　task　that　cannot　be　approached　using　classical　recon-struction　techniques.For　this　purpose,we　used　an　encoder-decoder　framework　to　encode　the　RGB　information　in　latent　space,and　to　predict　the　3D　structure　of　the　considered　object　from　different　viewpoints.The　individual　predic-tions　are　combined　to　yield　a　common　representation　that　is　used　in　a　module　combining　camera　pose　estimation　and　rendering,thereby　achieving　differentiability　with　respect　to　imaging　process　and　the　camera　pose,and　optimi-zation　of　the　two-dimensional　prediction　error　of　novel　viewpoints.Thus,our　method　allows　end-to-end　training　and　does　not　require　supervision　based　on　additional　ground-truth(GT)mask　annotations　or　ground-truth　camera　pose　annotations.Our　evaluation　of　synthetic　and　real-world　data　demonstrates　the　robustness　of　our　approach　to　appear-ance　changes　and　self-occlusions,through　outperformance　of　current　state-of-the-art　methods　in　terms　of　accuracy,density,and　model　completeness.