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KAPLAN: A 3D Point Descriptor for Shape Completion
We present a novel 3D shape completion method that operates directly on unstructured point clouds, thus avoiding resource-intensive data structures like voxel grids.
Learned Multi-View Texture Super-Resolution
We present a super-resolution method capable of creating a high-resolution texture map for a virtual 3D object from a set of lower-resolution images of that object.
Learned Semantic Multi-Sensor Depth Map Fusion
Our method learns sensor or algorithm properties jointly with semantic depth fusion and scene completion and can also be used as an expert system, e. g. to unify the strengths of various photometric stereo algorithms.
Learning Priors for Semantic 3D Reconstruction
In contrast to existing variational methods for semantic 3D reconstruction, our model is end-to-end trainable and captures more complex dependencies between the semantic labels and the 3D geometry.
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