Stereo　image　completion　(SIC)　is　to　fill　holes　existing　in　a　pair　of　stereo　images.　SIC　is　more　complicated　than　single　image　repairing,　which　needs　to　complete　the　pair　of　images　while　keeping　their　stereoscopic　consistency.　In　recent　years,　deep　learning　has　been　introduced　into　single　image　repairing　but　seldom　used　for　SIC.　The　authors　present　a　novel　deep　learning-based　approach　for　SIC.　In　their　method,　an　X-shaped　fully　convolutional　network　(called　SICNet)　is　proposed　and　designed　to　complete　stereo　images,　which　is　composed　of　two　branches　of　convolutional　neural　network　layers　to　encode　the　context　of　the　left　and　right　images　separately,　a　fusion　module　for　stereo-interactive　completion,　and　two　branches　of　decoders　to　produce　completed　left　and　right　images,　respectively.　In　consideration　of　both　inter-view　and　intra-view　cues,　they　introduce　auxiliary　networks　and　define　comprehensive　losses　to　train　SICNet　to　perform　single-view　coherent　and　cross-view　consistent　completion　simultaneously.　Extensive　experiments　are　conducted　to　show　the　state-of-the-art　performances　of　the　proposed　approach　and　its　key　components.　©　The　Institution　of　Engineering　and　Technology　2020