Lukas Rahmann — accepted AI-conference papers

2022

Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields

Wang Yifan, Lukas Rahmann, Olga Sorkine-hornung

ICLR 2022poster

We present implicit displacement fields, a novel representation for detailed 3D geometry. Inspired by a classic surface deformation technique, displacement mapping, our method represents a complex surface as a smooth base surface plus a displacement along the base's normal directions, resulting in a…

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2018

A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation

Riccardo Roveri, Lukas Rahmann, Cengiz Oztireli, Markus Gross

CVPR 2018poster

We propose a novel neural network architecture for point cloud classification. Our key idea is to automatically transform the 3D unordered input data into a set of useful 2D depth images, and classify them by exploiting well performing image classification CNNs. We present new differentiable module…

Cited by 82 Source ↗PDF ↗Scholar ↗