3D Instance Segmentation of MVS Buildings

We present a novel framework for instance segmentation of 3D buildings from Multi-view Stereo (MVS) urban scenes. Unlike existing works focusing on semantic segmentation of an urban scene, the emphasis of this work lies in detecting and segmenting 3D building instances even if they are attached and embedded in a large and imprecise 3D surface model. Multi-view RGB images are first enhanced to RGBH images by adding a heightmap and are segmented to obtain all roof instances using a fine-tuned 2D instance segmentation neural network. Roof instance masks from different multi-view images are then clustered into global masks. Our mask clustering accounts for spatial occlusion and overlapping, which can eliminate segmentation ambiguities among multi-view images. Based on these global masks, 3D roof instances are segmented out by mask back-projections and extended to the entire building instances through a Markov random field (MRF) optimization. Quantitative evaluations and ablation studies have shown the effectiveness of all major steps of the method. A dataset for the evaluation of instance segmentation of 3D building models is provided as well. To the best of our knowledge, it is the first dataset for 3D urban buildings on the instance segmentation level.