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Found 5 papers, 0 papers with code
Ordonnancement d'entités pour la rencontre du web des documents et du web des données
The advances of the Linked Open Data (LOD) initiative are giving rise to a more structured web of data.
DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD Models for 2.5D Recognition
Recent progress in computer vision has been dominated by deep neural networks trained over large amounts of labeled data.
Keep it Unreal: Bridging the Realism Gap for 2.5D Recognition with Geometry Priors Only
With the increasing availability of large databases of 3D CAD models, depth-based recognition methods can be trained on an uncountable number of synthetically rendered images.
Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition
Applying our approach to object recognition from texture-less CAD data, we present a custom generative network which fully utilizes the purely geometrical information to learn robust features and achieve a more refined mapping for unseen color images.
Incremental Scene Synthesis
We present a method to incrementally generate complete 2D or 3D scenes with the following properties: (a) it is globally consistent at each step according to a learned scene prior, (b) real observations of a scene can be incorporated while observing global consistency, (c) unobserved regions can be hallucinated locally in consistence with previous observations, hallucinations and global priors, and (d) hallucinations are statistical in nature, i. e., different scenes can be generated from the same observations.
