LassoNet: Deep Lasso-Selection of 3D Point Clouds

Chen, Zhutian; Zeng, Wei; Yang, Zhiguang; Yu, Lingyun; Fu, Chi‐Wing; Qu, Huamin

doi:10.1109/tvcg.2019.2934332

Cited by 44 publications

(19 citation statements)

References 39 publications

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“…In recent years, deep learning has been widely used in many fields of point clouds learning, including classification [15], [16], [17], [18], [19], segmentation [20], [21], [22], registration [23], [24], [25], denoising [26], [27], generation [28], completion [29], [30], [31], visualization [32], [33], etc. As the pioneer in applying neural networks to point cloud analysis, PointNet [15] and PointNet++ [16] propose to use shared MLP and symmetric functions as feature extractor.…”

Section: Deep Learning-based Upsampling Methodsmentioning

confidence: 99%

PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows

Mao¹,

Du²,

Hou³

et al. 2021

Preprint

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Point cloud upsampling aims to generate dense point clouds from given sparse ones, which is a challenging task due to the irregular and unordered nature of point sets. To address this issue, we present a novel deep learning-based model, called PU-Flow, which incorporates normalizing flows and feature interpolation techniques to produce dense points uniformly distributed on the underlying surface. Specifically, we formulate the upsampling process as point interpolation in a latent space, where the interpolation weights are adaptively learned from local geometric context, and exploit the invertible characteristics of normalizing flows to transform points between Euclidean and latent spaces. We evaluate PU-Flow on a wide range of 3D models with sharp features and high-frequency details. Qualitative and quantitative results show that our method outperforms state-of-the-art deep learning-based approaches in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency.

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Section: Deep Learning-based Upsampling Methodsmentioning

confidence: 99%

PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows

Mao¹,

Du²,

Hou³

et al. 2021

Preprint

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“…For example, Jung et al [25] trained a deep neural network to classify chart types. Chen et al [12] improved the effectiveness and robustness in selection of 3D point clouds using deep learning. Zhao et al [60] recommended charts for exploratory visual analysis using an interactive system coupled with machine intelligence.…”

Section: Related Workmentioning

confidence: 99%

Deep Colormap Extraction From Visualizations

Wen

Zeng

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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This work presents a new approach based on deep learning to automatically extract colormaps from visualizations. After summarizing colors in an input visualization image as a Lab color histogram, we pass the histogram to a pre-trained deep neural network, which learns to predict the colormap that produces the visualization. To train the network, we create a new dataset of ∼64K visualizations that cover a wide variety of data distributions, chart types, and colormaps. The network adopts an atrous spatial pyramid pooling module to capture color features at multiple scales in the input color histograms. We then classify the predicted colormap as discrete or continuous, and refine the predicted colormap based on its color histogram. Quantitative comparisons to existing methods show the superior performance of our approach on both synthetic and real-world visualizations. We further demonstrate the utility of our method with two use cases, i.e., color transfer and color remapping.

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“…Another perspective for improving our taxonomy is to expand the sub-categories by adding goals that are currently confined to a particular visualization. For instance, several systems propose machine learning methods for brushing point-based visualizations [145], [147], which falls under the visualization enhancement category.…”

Section: Whatmentioning

confidence: 99%

AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Wang

Shu

et al. 2021

Preprint

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Visualizations themselves have become a data format. Akin to other data formats such as text and images, visualizations are increasingly created, stored, shared, and (re-)used with artificial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data. We define visualization data as the digital representations of visualizations in computers and focus on visualizations in information visualization and visual analytic. We build our survey upon a corpus spanning ten different fields in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of surveyed papers and related material available online at ai4vis.github.io.

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LassoNet: Deep Lasso-Selection of 3D Point Clouds

Cited by 44 publications

References 39 publications

PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows

PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows

Deep Colormap Extraction From Visualizations

AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

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