Automated building change detection with amodal completion of point clouds

When updating digital models of existing buildings, changes in the built environment are detected by comparing outdated BIMs with captured point clouds representing current conditions. Here we show that point cloud completion (i.e. automated filling-in of missing data) improves the accuracy of change detection. We perform point cloud completion using a hierarchical deep variational autoencoder (a type of artificial neural network) modified to include skip connections between the convolution and deconvolution layers. The resulting receiver operating characteristic curve shows that completion boosts change detection performance from a total area under the curve of 0.55 to 0.75. Completion achieves this by eliminating differences between the BIM and point cloud inputs that are a consequence of incompleteness while distilling the differences due to building change. We anticipate that automated change detection methods with resilience to imperfect data will become more critical as automated building analyses become increasingly abstracted from data collection. •15 building information models used to generate synthetic training data.•Neural network automatically fills in missing parts of incomplete point clouds.•Point cloud completion improves performance of building change detection.