Data Consolidation and Information Aggregation in Grid Networks

Kokkinos, Panagiotis; Varvarigos, Emmanouel A.

doi:10.5772/13939

Cited by 3 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On CUB, they can handle open wings but otherwise only barely improve the coarse geometry beyond the quality of CMR. In the follow‐up TTP [KK21b], they turn around the correspondence regression of A‐CSM and IMR, instead regressing the 2D UV coordinate for every vertex of the mesh. TTP trains a shared network for the UV regression task but performs end‐to‐end differentiable, iterative instance optimization to determine the deformation and camera parameters at training time .…”

Section: State‐of‐the‐art Methodsmentioning

confidence: 99%

State of the Art in Dense Monocular Non‐Rigid 3D Reconstruction

Tretschk

Navami

Mallikarjun

et al. 2023

Computer Graphics Forum

View full text Add to dashboard Cite

3D reconstruction of deformable (or non‐rigid) scenes from a set of monocular 2D image observations is a long‐standing and actively researched area of computer vision and graphics. It is an ill‐posed inverse problem, since—without additional prior assumptions—it permits infinitely many solutions leading to accurate projection to the input 2D images. Non‐rigid reconstruction is a foundational building block for downstream applications like robotics, AR/VR, or visual content creation. The key advantage of using monocular cameras is their omnipresence and availability to the end users as well as their ease of use compared to more sophisticated camera set‐ups such as stereo or multi‐view systems. This survey focuses on state‐of‐the‐art methods for dense non‐rigid 3D reconstruction of various deformable objects and composite scenes from monocular videos or sets of monocular views. It reviews the fundamentals of 3D reconstruction and deformation modeling from 2D image observations. We then start from general methods—that handle arbitrary scenes and make only a few prior assumptions—and proceed towards techniques making stronger assumptions about the observed objects and types of deformations (e.g. human faces, bodies, hands, and animals). A significant part of this STAR is also devoted to classification and a high‐level comparison of the methods, as well as an overview of the datasets for training and evaluation of the discussed techniques. We conclude by discussing open challenges in the field and the social aspects associated with the usage of the reviewed methods.

show abstract

Section: State‐of‐the‐art Methodsmentioning

confidence: 99%

State of the Art in Dense Monocular Non‐Rigid 3D Reconstruction

Tretschk

Navami

Mallikarjun

et al. 2023

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…Information aggregation [2] relates to summarization of resource information in a Grid network and this information is given to resource manager in order to make scheduling decisions. As size of grid network grows, resource-related information size and dynamicity also grows rapidly, thus making the aggregation and use of this massive amount of information become a challenge for resource management system.…”

Section: Information Aggregationmentioning

confidence: 99%

Information Aggregation Techniques in Different Networks

ElsaShaji¹,

A²

2013

IJCA

View full text Add to dashboard Cite

Information aggregation is a method for reducing the information being exchanged between Grid networks. Resource manager takes the scheduling decisions by using this aggregated information. Aggregated information is kept across each node and the detailed information is kept private, but the resources are available publicly for use. This paper does a comparative study for some of the information aggregation techniques used in different networks.

show abstract

Domain based resource information aggregation in computational grid

Raja¹,

Noordeen²

2017

2017 International Conference on Intelligent Sustainable Systems (ICISS)

View full text Add to dashboard Cite

Data Consolidation and Information Aggregation in Grid Networks

Cited by 3 publications

References 28 publications

State of the Art in Dense Monocular Non‐Rigid 3D Reconstruction

State of the Art in Dense Monocular Non‐Rigid 3D Reconstruction

Information Aggregation Techniques in Different Networks

Domain based resource information aggregation in computational grid

Contact Info

Product

Resources

About