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Found 8 papers, 1 papers with code
Automatic Content-aware Non-Photorealistic Rendering of Images
Non-photorealistic rendering techniques work on image features and often manipulate a set of characteristics such as edges and texture to achieve a desired depiction of the scene.
GRAINS: Generative Recursive Autoencoders for INdoor Scenes
We present a generative neural network which enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently.
Graphics
READ: Recursive Autoencoders for Document Layout Generation
Creating large varieties of plausible document layouts can be a tedious task, requiring numerous constraints to be satisfied, including local ones relating different semantic elements and global constraints on the general appearance and spacing.
DR-KFS: A Differentiable Visual Similarity Metric for 3D Shape Reconstruction
We introduce a differential visual similarity metric to train deep neural networks for 3D reconstruction, aimed at improving reconstruction quality.
LayoutGMN: Neural Graph Matching for Structural Layout Similarity
In particular, retrieval results by our network better match human judgement of structural layout similarity compared to both IoUs and other baselines including a state-of-the-art method based on graph neural networks and image convolution.
Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images
We introduce the first active learning (AL) framework for high-accuracy instance segmentation of moveable parts from RGB images of real indoor scenes.
RoSI: Recovering 3D Shape Interiors from Few Articulation Images
The dominant majority of 3D models that appear in gaming, VR/AR, and those we use to train geometric deep learning algorithms are incomplete, since they are modeled as surface meshes and missing their interior structures.
DiViNeT: 3D Reconstruction from Disparate Views via Neural Template Regularization
We demonstrate that our approach is not only able to complete the surface geometry but also reconstructs surface details to a reasonable extent from a few disparate input views.
