VisDecode: AI-Driven Interpretation and Enhancement of Scientific Plots
+DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling
VisDecode: AI-Driven Interpretation and
- VisDecode is a project to create an AI tool capable of automatically interpreting and providing feedback on scientific plots. Utilizing state-of-the-art visual language understanding techniques, VisDecode analyzes raster images of plots such as bar charts, line charts, and scatter plots. It extracts key visual attributes like color, shape, positioning, and plot data, all of which significantly impact data perception and understanding. Based on these analyses and well-established best practices from data visualization literature, VisDecode offers actionable suggestions for improving the design of these plots. This feedback helps ensure that scientific visualizations are both clear and effective in communicating data. A significant advantage of VisDecode is its framework-free nature, allowing scientists to continue using their preferred visualization tools while still benefiting from AI-driven design enhancements. By incorporating these expert recommendations, VisDecode empowers researchers to create better data visualizations.
+ DUDF is able to leverage general-shape neural representation by learning a hyperbolic scaled unsigned distance field.
+ The learning process is governed by solving a new and interesting Eikonal problem.
+ This allows for detailed reconstructions, and extraction of important topological properties such as normal fields and shape curvatures; which were evasive in previous works.
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+ Abstract
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+ In recent years, there has been a growing interest in training Neural Networks to approximate Unsigned Distance Fields (UDFs) for representing open surfaces in the context of 3D reconstruction.
+ However, UDFs are non-differentiable at the zero level set which leads to significant errors in distances and gradients, generally resulting in fragmented and discontinuous surfaces.
+ In this paper, we propose to learn a hyperbolic scaling of the unsigned distance field, which defines a new Eikonal problem with distinct boundary conditions.
+ This allows our formulation to integrate seamlessly with state-of-the-art continuously differentiable implicit neural representation networks, largely applied in the literature to represent signed distance fields.
+ Our approach not only addresses the challenge of open surface representation but also demonstrates significant improvement in reconstruction quality and training performance.
+ Moreover, the unlocked field's differentiability allows the accurate computation of essential topological properties such as normal directions and curvatures, pervasive in downstream tasks such as rendering.
+ Through extensive experiments, we validate our approach across various data sets and against competitive baselines.
+ The results demonstrate enhanced accuracy and up to an order of magnitude increase in speed compared to previous methods.
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+ Baseline comparisons
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+ We compare our method to state of the art neural representation approaches in three common open-surface datasets. Results show greater accuracy and improved training times.
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+ Topological properties
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+ The full differentiability of our method allows for mean and gaussian curvature computation.
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+ Direct rendering and ilumination
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+ Precise normal field and principal curvatures computation allows for realistic direct rendering techniques.
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BibTeX
- @misc{author2024visdecode,
- title={VisDecode: AI-Driven Interpretation and Enhancement of Scientific Plots},
- author={Author One and Author Two and Author Three},
+ @misc{fainstein2024dudf,
+ title={DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling},
+ author={Miguel Fainstein and Viviana Siless and Emmanuel Iarussi},
year={2024},
- eprint={xxxx.xxxxx},
+ eprint={2402.08876},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@@ -123,10 +258,11 @@ BibTeX
+
- VisDecode is a project to create an AI tool capable of automatically interpreting and providing feedback on scientific plots. Utilizing state-of-the-art visual language understanding techniques, VisDecode analyzes raster images of plots such as bar charts, line charts, and scatter plots. It extracts key visual attributes like color, shape, positioning, and plot data, all of which significantly impact data perception and understanding. Based on these analyses and well-established best practices from data visualization literature, VisDecode offers actionable suggestions for improving the design of these plots. This feedback helps ensure that scientific visualizations are both clear and effective in communicating data. A significant advantage of VisDecode is its framework-free nature, allowing scientists to continue using their preferred visualization tools while still benefiting from AI-driven design enhancements. By incorporating these expert recommendations, VisDecode empowers researchers to create better data visualizations. + DUDF is able to leverage general-shape neural representation by learning a hyperbolic scaled unsigned distance field. + The learning process is governed by solving a new and interesting Eikonal problem. + This allows for detailed reconstructions, and extraction of important topological properties such as normal fields and shape curvatures; which were evasive in previous works.
+ Abstract
++ In recent years, there has been a growing interest in training Neural Networks to approximate Unsigned Distance Fields (UDFs) for representing open surfaces in the context of 3D reconstruction. + However, UDFs are non-differentiable at the zero level set which leads to significant errors in distances and gradients, generally resulting in fragmented and discontinuous surfaces. + In this paper, we propose to learn a hyperbolic scaling of the unsigned distance field, which defines a new Eikonal problem with distinct boundary conditions. + This allows our formulation to integrate seamlessly with state-of-the-art continuously differentiable implicit neural representation networks, largely applied in the literature to represent signed distance fields. + Our approach not only addresses the challenge of open surface representation but also demonstrates significant improvement in reconstruction quality and training performance. + Moreover, the unlocked field's differentiability allows the accurate computation of essential topological properties such as normal directions and curvatures, pervasive in downstream tasks such as rendering. + Through extensive experiments, we validate our approach across various data sets and against competitive baselines. + The results demonstrate enhanced accuracy and up to an order of magnitude increase in speed compared to previous methods. +
+Baseline comparisons
+
+ We compare our method to state of the art neural representation approaches in three common open-surface datasets. Results show greater accuracy and improved training times.
+
+
Topological properties
+
+ The full differentiability of our method allows for mean and gaussian curvature computation.
+
+
+
+ Mean
+
+ Gaussian
+Direct rendering and ilumination
++ Precise normal field and principal curvatures computation allows for realistic direct rendering techniques. +
+
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+
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+ BibTeX
- @misc{author2024visdecode,
- title={VisDecode: AI-Driven Interpretation and Enhancement of Scientific Plots},
- author={Author One and Author Two and Author Three},
+ @misc{fainstein2024dudf,
+ title={DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling},
+ author={Miguel Fainstein and Viviana Siless and Emmanuel Iarussi},
year={2024},
- eprint={xxxx.xxxxx},
+ eprint={2402.08876},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@@ -123,10 +258,11 @@ BibTeX
BibTeX
+ diff --git a/migue.html b/migue.html new file mode 100644 index 0000000..4210d4c --- /dev/null +++ b/migue.html @@ -0,0 +1,288 @@ + + + + + + + +DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling
+ + + + +
+ Abstract
++ In recent years, there has been a growing interest in training Neural Networks to approximate Unsigned Distance Fields (UDFs) for representing open surfaces in the context of 3D reconstruction. + However, UDFs are non-differentiable at the zero level set which leads to significant errors in distances and gradients, generally resulting in fragmented and discontinuous surfaces. + In this paper, we propose to learn a hyperbolic scaling of the unsigned distance field, which defines a new Eikonal problem with distinct boundary conditions. + This allows our formulation to integrate seamlessly with state-of-the-art continuously differentiable implicit neural representation networks, largely applied in the literature to represent signed distance fields. + Our approach not only addresses the challenge of open surface representation but also demonstrates significant improvement in reconstruction quality and training performance. + Moreover, the unlocked field's differentiability allows the accurate computation of essential topological properties such as normal directions and curvatures, pervasive in downstream tasks such as rendering. + Through extensive experiments, we validate our approach across various data sets and against competitive baselines. + The results demonstrate enhanced accuracy and up to an order of magnitude increase in speed compared to previous methods. +
+Baseline comparisons
+
+ We compare our method to state of the art neural representation approaches in three common open-surface datasets. Results show greater accuracy and improved training times.
+
+
Topological properties
+
+ The full differentiability of our method allows for mean and gaussian curvature computation.
+
+
+
+ Mean
+
+ Gaussian
+Direct rendering and ilumination
++ Precise normal field and principal curvatures computation allows for realistic direct rendering techniques. +
+
+
+
+
+ BibTeX
+
+ @misc{fainstein2024dudf,
+ title={DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling},
+ author={Miguel Fainstein and Viviana Siless and Emmanuel Iarussi},
+ year={2024},
+ eprint={2402.08876},
+ archivePrefix={arXiv},
+ primaryClass={cs.CV}
+ }
+