3D piece-wise planar reconstruction from a single indoor image based on self-augmented-attention mechanism
The piece-wise 3D reconstruction of indoor scenes using convolutional neural networks(CNN)has become one of the hot topics in the research of indoor scene modeling.However,the intertwining of planar and non-planar elements often leads to the network's extraction of non-planar information mixed with planar features,thereby affecting the final segmentation accuracy.Moreover,there are significant scale differences in the planes present in indoor scenes,leading to pronounced class imbalances,where small-scale plane instances are prone to distortion.To address these challenges,this paper proposed a self-enhanced attention-based multi-scale feature fusion network for 3D plane segmentation reconstruction.This network can automatically learn planar features in the scene and effectively fuse feature information from different scales,thereby enhancing the accuracy of plane instance segmentation.At the same time,by assigning different weights to each pixel in the plane instance,particularly increasing the weight values for small-scale plane edge pixels,the channel representation of small-scale plane segmentation objects was further enhanced.Finally,a new loss function was constructed using balanced cross-entropy loss and dice loss to train the model,further improving the accuracy of plane segmentation.Extensive experiments demonstrated that the algorithm proposed achieves significant improvements in plane recall rate and segmentation accuracy,resulting in more accurate indoor 3D segmented plane reconstruction models.
deep learningsegmented plane reconstructionmulti-scale fusionenhance attentionself-attention
NETL
NSTL
万方数据
