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Consolidation of unorganized point clouds for surface reconstruction
TLDR
A point cloud with noise, outliers, non-uniformities, and in particular interference between close-by surface sheets is consolidated as a preprocess to surface generation, focusing on reliable normal estimation. Expand
Edge-aware point set resampling
TLDR
The Edge-Aware Resampling algorithm is demonstrated to be capable of producing consolidated point sets with noise-free normals and clean preservation of sharp features, and to lead to improved performance of edge-aware reconstruction methods and point set rendering techniques. Expand
Patch-Based Progressive 3D Point Set Upsampling
TLDR
Qualitative and quantitative experiments show that the method significantly outperforms the state-of-the-art learning-based and optimazation-based approaches, both in terms of handling low-resolution inputs and revealing high-fidelity details. Expand
L1-medial skeleton of point cloud
TLDR
A L1-medial skeleton construction algorithm is developed which can be directly applied to an unoriented raw point scan with significant noise, outliers, and large areas of missing data. Expand
Non-stationary texture synthesis by adversarial expansion
TLDR
This paper proposes a new approach for example-based non-stationary texture synthesis that uses a generative adversarial network (GAN), trained to double the spatial extent of texture blocks extracted from a specific texture exemplar, and demonstrates that it can cope with challenging textures, which no other existing method can handle. Expand
Global-to-local generative model for 3D shapes
TLDR
It is demonstrated that the global-to-local generative model produces significantly better results than a plain three-dimensional GAN, in terms of both their shape variety and the distribution with respect to the training data. Expand
Quality-driven poisson-guided autoscanning
TLDR
A quality-driven, Poisson-guided autonomous scanning method based on the analysis of a Poisson field and its geometric relation with an input scan to ensure the high quality scanning of the model. Expand
Cascaded Feature Network for Semantic Segmentation of RGB-D Images
TLDR
This paper presents a neural network with multiple branches for segmenting RGB-D images, and introduces context-aware receptive field (CaRF) which provides a better control on the relevant contextual information of the learned features. Expand
Exploring Visual Information Flows in Infographics
TLDR
This work uses a deep neural network to identify visual elements related to information, agnostic to their various artistic appearances, and characterize the VIF design space by a taxonomy of 12 different design patterns. Expand
Multi-scale Context Intertwining for Semantic Segmentation
TLDR
This work proposes a novel scheme for aggregating features from different scales, which it refers to as Multi-Scale Context Intertwining (MSCI), which merge pairs of feature maps in a bidirectional and recurrent fashion, via connections between two LSTM chains. Expand
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