To perceive indoor spatial layout,we present a scene layout estimation method based on informative edges and multi-modality features.First,the VGG-16 full convolutional neural network is applied to predict informative edge map with the prior of spatial layout.Then,Canny edge detection and voting strategy are utilized to estimate the horizontal and vertical vanishing points,while the rays led at equal intervals from the given vanishing points finely resample the divided regions with high informative edge energies for the layout candidates.Next,the spatial multi-scaled VGG-16-based convolutional neural network is adopted to estimate the related geometric depth and normal vectors on the scene surfaces.And then,integral geometry is applied to accumulate the multi-model regional features as unary occurrence potential in the polygons of candidate layouts,and the pairwise label constrains are reflected by surface normal smooth and the location relationship of candidate layouts.Finally,the mode parameters can be learned by structural SVM learning,and the scene layout can be inferred by maximizing the related scores of the layout candidates.Experimental results show that,compared with traditional methods,this proposed estimation method can effectively improve the completeness of the resulting spatial layouts.