SceneNet: An annotated model generator for indoor scene understanding
| Autor: | Roberto Cipolla, Viorica Patraucean, Simon Stent, Ankur Handa |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry 3D reconstruction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Object (computer science) Machine learning computer.software_genre Task (project management) 0202 electrical engineering electronic engineering information engineering RGB color model 020201 artificial intelligence & image processing Computer vision Artificial intelligence business computer ComputingMethodologies_COMPUTERGRAPHICS Generator (mathematics) |
| Zdroj: | ICRA |
| DOI: | 10.1109/icra.2016.7487797 |
| Popis: | We introduce SceneNet, a framework for generating high-quality annotated 3D scenes to aid indoor scene understanding. SceneNet leverages manually-annotated datasets of real world scenes such as NYUv2 to learn statistics about object co-occurrences and their spatial relationships. Using a hierarchical simulated annealing optimisation, these statistics are exploited to generate a potentially unlimited number of new annotated scenes, by sampling objects from various existing databases of 3D objects such as ModelNet, and textures such as OpenSurfaces and ArchiveTextures. Depending on the task, SceneNet can be used directly in the form of annotated 3D models for supervised training and 3D reconstruction benchmarking, or in the form of rendered annotated sequences of RGB-D frames or videos. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|