13,550 research outputs found
Visualizing the Results of a Complex Hybrid Dynamic-Static Analysis
Complex static or hybrid static-dynamic analyses produce large quantities of structured data. In the past, this data was generally intended for use by compilers or other software tools that used the produced information to transform the application being analyzed. However, it is becomingly increasingly common for the results of these analyses to be used directly by humans. For example, in our own prior work we have developed a hybrid dynamic-static escape analysis intended to help developers identify sources of object churn within large framework-base applications. In order to facilitate human use of complex analysis results, visualizations need to be developed that allow a user to browse these results and to identify the points of interest within these large data sets. In this paper we present Hi-C, a visualization tool for our hybrid escape analysis that has been implemented as an Eclipse plugin. We show how Hi-C can help developers identify sources of object churn in a large framework-based application and how we have used the tool to assist in understanding the results of a complex analysis
Learning a Hierarchical Latent-Variable Model of 3D Shapes
We propose the Variational Shape Learner (VSL), a generative model that
learns the underlying structure of voxelized 3D shapes in an unsupervised
fashion. Through the use of skip-connections, our model can successfully learn
and infer a latent, hierarchical representation of objects. Furthermore,
realistic 3D objects can be easily generated by sampling the VSL's latent
probabilistic manifold. We show that our generative model can be trained
end-to-end from 2D images to perform single image 3D model retrieval.
Experiments show, both quantitatively and qualitatively, the improved
generalization of our proposed model over a range of tasks, performing better
or comparable to various state-of-the-art alternatives.Comment: Accepted as oral presentation at International Conference on 3D
Vision (3DV), 201
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