IfcVoxNet 3D segmentation and classification of voxelized IFC building models with Deep Learning Johan Luttun, [email protected] Thomas Krijnen, [email protected] Presenter: Artur Tomczak CIB W78 2024 Marrakesh, Morocco 

Context: Examples of misclassifications in BIM models Elements classified as IfcWall in the Duplex_A model. Source: Krijnen, T., & Tamke, M. (2015). Assessing implicit knowledge in BIM models with machine learning. In Modelling Behaviour: Design Modelling Symposium 2015 (pp. 397-406). Springer International Publishing. 

Context: Advanced automated use cases requiring accurate classifications Quantity takeoffs Building permit checks Simulations … 

Context: Enabling more granular classifications Regulations on the height of door handles, automatic doors closers, … IfcDoor Hinge Handle 0.85 < d < 0.95 

The importance of geometric context Ambiguity: wall, beam, column, railing, or just wall? Problem definition: BIM elements have little discriminating geometric features, but require context to interpret their role 

Modeler, software, LOD differences Problem definition: Geometry and topology of elements varies based on modeler, software and LOD. 

Meanwhile in 2D image recognition “Chihuahua or muffin” (source unknown) How can we benefit from advancements in convolutional neural networks with Voxels as a “3D pixels”? 

Other uses for voxelization https://github.com/opensourceBIM/ voxelization_toolkit 

Network architecture 3D U-Net architecture Çiçek, Özgün, et al. "3D U-Net: learning dense volumetric segmentation from sparse annotation." Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19. Springer International Publishing, 2016. 

Approach 1. Voxelize IFC models. 1 voxel belongs to 1 class (order of priority set) 2. Train/test splitting 3. Create 64x64x64 3D patches from individual instances bbox centers 4. Train with weighted Cross-entropy loss 5. Use the model in inference 

Dataset 

Dataset 

Results presented graphically Confusion at the boundaries between elements. Network ‘expects’ window to come with window sill. wall window slab stair beam railing error 

Results presented graphically Staircase accurately predicted, but the ‘social stairs’ is not recognized as a stair (it most likely also isn’t). wall window slab stair beam railing error 

Results presented graphically Confusion between whether the final step of the staircase is part of the stair or part of the floor slab. wall window slab stair beam railing error 

Results confusion table Per-voxel Model: FZK-Haus 

Results confusion table Per-element Model: Molio 

Results confusion table Per-element Model: FZK-Haus 

Conclusions and next steps - Promising results for further research and applications - Test instance segmentation (door knob, door closers) - Find the ideal patch size (#voxels) and voxel size (cm) - Add material characteristics to voxels in addition to class - Use multiclass per voxel - Anomaly detection 

Anomaly detection boredpanda.com 

IfcVoxNet 3D segmentation and classification of voxelized IFC building models with Deep Learning Johan Luttun, [email protected] Thomas Krijnen, [email protected] Presenter: Artur Tomczak CIB W78 2024 Marrakesh, Morocco