182 research outputs found
ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylization
The radiance fields style transfer is an emerging field that has recently
gained popularity as a means of 3D scene stylization, thanks to the outstanding
performance of neural radiance fields in 3D reconstruction and view synthesis.
We highlight a research gap in radiance fields style transfer, the lack of
sufficient perceptual controllability, motivated by the existing concept in the
2D image style transfer. In this paper, we present ARF-Plus, a 3D neural style
transfer framework offering manageable control over perceptual factors, to
systematically explore the perceptual controllability in 3D scene stylization.
Four distinct types of controls - color preservation control, (style pattern)
scale control, spatial (selective stylization area) control, and depth
enhancement control - are proposed and integrated into this framework. Results
from real-world datasets, both quantitative and qualitative, show that the four
types of controls in our ARF-Plus framework successfully accomplish their
corresponding perceptual controls when stylizing 3D scenes. These techniques
work well for individual style inputs as well as for the simultaneous
application of multiple styles within a scene. This unlocks a realm of
limitless possibilities, allowing customized modifications of stylization
effects and flexible merging of the strengths of different styles, ultimately
enabling the creation of novel and eye-catching stylistic effects on 3D scenes
Neural Radiance Fields: Past, Present, and Future
The various aspects like modeling and interpreting 3D environments and
surroundings have enticed humans to progress their research in 3D Computer
Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall
et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in
Computer Graphics, Robotics, Computer Vision, and the possible scope of
High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D
models have gained traction from res with more than 1000 preprints related to
NeRFs published. This paper serves as a bridge for people starting to study
these fields by building on the basics of Mathematics, Geometry, Computer
Vision, and Computer Graphics to the difficulties encountered in Implicit
Representations at the intersection of all these disciplines. This survey
provides the history of rendering, Implicit Learning, and NeRFs, the
progression of research on NeRFs, and the potential applications and
implications of NeRFs in today's world. In doing so, this survey categorizes
all the NeRF-related research in terms of the datasets used, objective
functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation
Computer Assisted Relief Generation - a Survey
In this paper we present an overview of the achievements accomplished to date in the field of computer aided relief
generation. We delineate the problem, classify the different solutions, analyze similarities, investigate the evelopment and review the approaches according to their particular relative strengths and weaknesses. In consequence this survey is likewise addressed to researchers and artists through providing valuable insights into the theory behind the different concepts in this field and augmenting the options available among the methods presented with regard to practical application
Revealing the Invisible: On the Extraction of Latent Information from Generalized Image Data
The desire to reveal the invisible in order to explain the world around us has been a source of impetus for technological and scientific progress throughout human history. Many of the phenomena that directly affect us cannot be sufficiently explained based on the observations using our primary senses alone. Often this is because their originating cause is either too small, too far away, or in other ways obstructed. To put it in other words: it is invisible to us. Without careful observation and experimentation, our models of the world remain inaccurate and research has to be conducted in order to improve our understanding of even the most basic effects. In this thesis, we1 are going to present our solutions to three challenging problems in visual computing, where a surprising amount of information is hidden in generalized image data and cannot easily be extracted by human observation or existing methods. We are able to extract the latent information using non-linear and discrete optimization methods based on physically motivated models and computer graphics methodology, such as ray tracing, real-time transient rendering, and image-based rendering
Faceted Search of Heterogeneous Geographic Information for Dynamic Map Projection
This paper proposes a faceted information exploration model that supports
coarse-grained and fine-grained focusing of geographic maps by offering a
graphical representation of data attributes within interactive widgets. The
proposed approach enables (i) a multi-category projection of long-lasting
geographic maps, based on the proposal of efficient facets for data exploration
in sparse and noisy datasets, and (ii) an interactive representation of the
search context based on widgets that support data visualization, faceted
exploration, category-based information hiding and transparency of results at
the same time. The integration of our model with a semantic representation of
geographical knowledge supports the exploration of information retrieved from
heterogeneous data sources, such as Public Open Data and OpenStreetMap. We
evaluated our model with users in the OnToMap collaborative Web GIS. The
experimental results show that, when working on geographic maps populated with
multiple data categories, it outperforms simple category-based map projection
and traditional faceted search tools, such as checkboxes, in both user
performance and experience
Scalable visualization of spatial data in 3D terrain
Designing visualizations of spatial data in 3D terrain is challenging because various heterogeneous data aspects need to be considered, including the terrain itself, multiple data attributes, and data uncertainty. It is hardly possible to visualize these data at full detail in a single image. Therefore, this thesis devises a scalable visualization approach that focuses on relevant information to be emphasized, while less-relevant information can be attenuated. In this context, a noval concept of visualizing spatial data in 3D terrain and different soft- and hardware solutions are proposed.Die Erstellung von Visualisierungen fĂŒr rĂ€umliche Daten im 3D-GelĂ€nde ist schwierig, da viele heterogene Datenaspekte wie das GelĂ€nde selbst, die verschiedenen Datenattribute sowie Unsicherheiten bei der Darstellung zu berĂŒcksichtigen sind. Im Allgemeinen ist es nicht möglich, diese Datenaspekte gleichzeitig in einer Visualisierung darzustellen. Daher werden in der Arbeit skalierbare Visualisierungsstrategien entwickelt, welche die wichtigen Informationen hervorheben und trotzdem gleichzeitig Kontextinformationen liefern. HierfĂŒr werden neue Systematisierungen und Konzepte vorgestellt
Visual analytics methods for retinal layers in optical coherence tomography data
Optical coherence tomography is an important imaging technology for the early detection of ocular diseases. Yet, identifying substructural defects in the 3D retinal images is challenging. We therefore present novel visual analytics methods for the exploration of small and localized retinal alterations. Our methods reduce the data complexity and ensure the visibility of relevant information. The results of two cross-sectional studies show that our methods improve the detection of retinal defects, contributing to a deeper understanding of the retinal condition at an early stage of disease.Die optische KohĂ€renztomographie ist ein wichtiges Bildgebungsverfahren zur FrĂŒherkennung von Augenerkrankungen. Die Identifizierung von substrukturellen Defekten in den 3D-Netzhautbildern ist jedoch eine Herausforderung. Wir stellen daher neue Visual-Analytics-Methoden zur Exploration von kleinen und lokalen NetzhautverĂ€nderungen vor. Unsere Methoden reduzieren die DatenkomplexitĂ€t und gewĂ€hrleisten die Sichtbarkeit relevanter Informationen. Die Ergebnisse zweier Querschnittsstudien zeigen, dass unsere Methoden die Erkennung von Netzhautdefekten in frĂŒhen Krankheitsstadien verbessern
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