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Large-Scale Semantic 3D Reconstruction

project was presented

both at the orals and as a poster. What it does is automatic generation

of semantically annotated

3D city models

, starting from large image

collections. The idea of semantic 3D reconstruction is to recover the

geometry of an observed scene while at the same time also

interpreting the scene in terms of semantic object classes (such as

buildings, vegetation etc.).

While jointly inferring 3D shapes and semantic classes delivers

appealing results, up-to-date methods are memory-hungry and

computationally expensive. This approach demonstrates how to scale

semantic 3D reconstruction up to be able to reconstruct large

geographic areas such as cities. The resulting superior 3D models allow

for advanced reasoning tasks such as urban planning, navigation,

physics-based simulations and the like.

This method is not only limited to 3D city modelling: possible further

applications include a number of multi-class segmentation problems,

e.g. in the field of medical imaging. This six-teamer work is available in


here .

CVPR Daily: Thursday



Maros Blaha and Audrey Richard

CVPR Daily: Thursday