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Found 7 papers, 5 papers with code
Classifying point clouds at the facade-level using geometric features and deep learning networks
3D building models with facade details are playing an important role in many applications now.
Transferring facade labels between point clouds with semantic octrees while considering change detection
Point clouds and high-resolution 3D data have become increasingly important in various fields, including surveying, construction, and virtual reality.
Reconstructing facade details using MLS point clouds and Bag-of-Words approach
We propose a new approach for the reconstruction of 3D fa\c{c}ade details.
MLS2LoD3: Refining low LoDs building models with MLS point clouds to reconstruct semantic LoD3 building models
Although highly-detailed LoD3 building models reveal great potential in various applications, they have yet to be available.
Scan2LoD3: Reconstructing semantic 3D building models at LoD3 using ray casting and Bayesian networks
To this end, we leverage laser physics and 3D building model priors to probabilistically identify model conflicts.
TUM-FAÇADE: Reviewing and enriching point cloud benchmarks for façade segmentation
Point clouds are widely regarded as one of the best dataset types for urban mapping purposes.
Combining visibility analysis and deep learning for refinement of semantic 3D building models by conflict classification
The semantic voxels and conflicts are combined in a Bayesian network to classify and delineate fa\c{c}ade openings, which are reconstructed using a 3D model library.
