arXiv subjects

Nicolas Mellado

Publications and source records attributed to Nicolas Mellado.

5 recordsLinked to original sources

arXiv2023

Challenges and Opportunities of Content Optimization for Freeform User Interfaces

While recent innovations on shape technologies allow for the creation of displays with almost unlimited form factors, current graphical user interfaces still rely on rectangular layouts and contents. This rectangular legacy hinders the progress of freeform displays, which are particularly relevant for pervasive scenarios to display interactive dynamic content where and when needed. By challenging the prevailing layout tradition on rectangular displays, freeform user interfaces raise design challenges which call for exploring the interlink between computational approaches and user interface generation and adaptation. In this position paper we report on previous work on content optimization for freeform user interfaces and anticipate the upcoming challenges and opportunities.

cs.HC↗

arXiv2021

PCEDNet : A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds

In recent years, Convolutional Neural Networks (CNN) have proven to be efficient analysis tools for processing point clouds, e.g., for reconstruction, segmentation and classification. In this paper, we focus on the classification of edges in point clouds, where both edges and their surrounding are described. We propose a new parameterization adding to each point a set of differential information on its surrounding shape reconstructed at different scales. These parameters, stored in a Scale-Space Matrix (SSM), provide a well suited information from which an adequate neural network can learn the description of edges and use it to efficiently detect them in acquired point clouds. After successfully applying a multi-scale CNN on SSMs for the efficient classification of edges and their neighborhood, we propose a new lightweight neural network architecture outperforming the CNN in learning time, processing time and classification capabilities. Our architecture is compact, requires small learning sets, is very fast to train and classifies millions of points in seconds.

cs.GR↗

arXiv2020

Code Replicability in Computer Graphics

Being able to duplicate published research results is an important process of conducting research whether to build upon these findings or to compare with them. This process is called "replicability" when using the original authors' artifacts (e.g., code), or "reproducibility" otherwise (e.g., re-implementing algorithms). Reproducibility and replicability of research results have gained a lot of interest recently with assessment studies being led in various fields, and they are often seen as a trigger for better result diffusion and transparency. In this work, we assess replicability in Computer Graphics, by evaluating whether the code is available and whether it works properly. As a proxy for this field we compiled, ran and analyzed 151 codes out of 374 papers from 2014, 2016 and 2018 SIGGRAPH conferences. This analysis shows a clear increase in the number of papers with available and operational research codes with a dependency on the subfields, and indicates a correlation between code replicability and citation count. We further provide an interactive tool to explore our results and evaluation data.

cs.DL↗

arXiv2019

RGB Point Cloud Manipulation with Triangular Structures for Artistic Image Recoloring

Usual approaches for image recoloring, such as local filtering by transfer functions and global histogram remapping, lack of accurate control or miss small groups of important pixels. In this paper, we introduce a triangle-based structuring of the colors of an image in the RGB space. We present an analysis of image colors in the RGB space showing the theoretical motivation of our triangular abstraction. We illustrate the usefulness of our structure to recolor images.

cs.GR↗

arXiv2019

Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers

Single-photon lidar has emerged as a prime candidate technology for depth imaging through challenging environments. Until now, a major limitation has been the significant amount of time required for the analysis of the recorded data. Here we show a new computational framework for real-time three-dimensional (3D) scene reconstruction from single-photon data. By combining statistical models with highly scalable computational tools from the computer graphics community, we demonstrate 3D reconstruction of complex outdoor scenes with processing times of the order of 20 ms, where the lidar data was acquired in broad daylight from distances up to 320 metres. The proposed method can handle an unknown number of surfaces in each pixel, allowing for target detection and imaging through cluttered scenes. This enables robust, real-time target reconstruction of complex moving scenes, paving the way for single-photon lidar at video rates for practical 3D imaging applications.

eess.IV↗