RT Conference Proceedings
T1 Enhanced Perspective Generation by Consensus of NeX neural models
A1 Pacheco dos Santos Lima Junior, Marcos Sergio
A1 Fernández-Rodríguez, Jose David
A1 Ortiz-de-Lazcano-Lobato, Juan Miguel
A1 López-Rubio, Ezequiel
A1 Domínguez-Merino, Enrique
K1 Redes neuronales (Informática) - Congresos
K1 Aprendizaje automático (Inteligencia artificial) - Congresos
K1 Sistemas autoorganizativos - Congresos
AB Neural rendering is a relatively new field of research that aims to produce high quality perspectives of a 3D scene from a reduced set of sample images. This is done with the help of deep artificial neural networks that model the geometry and color characteristics of the scene. The NeX model relies on neural basis expansion to yield accurate results with a lower computational load than the previous NeRF model. In this work, a procedure is proposed to further enhance the quality of the perspectives generated by NeX. Our proposal is based on the combination of the outputs of several NeX models by a consensus mechanism. The approach is compared to the original NeX for a wide range of scenes. It is found that our method significantly outperforms the original procedure, both in quantitative and qualitative terms.
YR 2022
FD 2022-07
LK https://hdl.handle.net/10630/24769
UL https://hdl.handle.net/10630/24769
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026