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Mattias P Heinrich

3 accepted papers

2023

Chasing Clouds: Differentiable Volumetric Rasterisation of Point Clouds as a Highly Efficient and Accurate Loss for Large-Scale Deformable 3D Registration

ICCV 2023oral

Learning-based registration for large-scale 3D point clouds has been shown to improve robustness and accuracy compared to classical methods and can be trained without supervision for locally rigid problems. However, for tasks with highly deformable structures, such as alignment of pulmonary vascular…

Cited by 10PDFcodeScholar
2023

Lung250M-4B: A Combined 3D Dataset for CT- and Point Cloud-Based Intra-Patient Lung Registration

NeurIPS 2023poster

A popular benchmark for intra-patient lung registration is provided by the DIR-LAB COPDgene dataset consisting of large-motion in- and expiratory breath-hold CT pairs. This dataset alone, however, does not provide enough samples to properly train state-of-the-art deep learning methods. Other public…

Cited by 8SourcePDFScholar
2023

Why Is the Winner the Best?

CVPR 2023poster

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and…

Cited by 29SourcePDFScholar