Adrian Penate-Sanchez — accepted AI-conference papers

2026

BEA-GS: BEyond RAdiance Supervision in 3DGS for Precise Object Extraction

Alessio Mazzucchelli, Maria Naranjo-Almeida, Jorge Bustos-Sanchez, Mariella Dimiccoli, Francesc Moreno-Noguer, Jordi Sanchez-Riera +1

CVPR 2026

Most Gaussian Splatting techniques that provide a 3D semantic representation of the scene don't optimize the underlying 3D geometry of the scene. This makes object-level editing or asset extraction challenging. Recent methods, like COBGS, Trace3D, and ObjectGS, acknowledge this limitation and propos

Cited by 0 Source ↗Scholar ↗

2024

IReNe: Instant Recoloring of Neural Radiance Fields

Alessio Mazzucchelli, Adrian Garcia-Garcia, Elena Garces, Fernando Rivas-Manzaneque, Francesc Moreno-Noguer, Adrian Penate-Sanchez

CVPR 2024poster

Advances in NERFs have allowed for 3D scene reconstructions and novel view synthesis. Yet efficiently editing these representations while retaining photorealism is an emerging challenge. Recent methods face three primary limitations: they're slow for interactive use lack precision at object boundari…

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2023

NeRFLight: Fast and Light Neural Radiance Fields Using a Shared Feature Grid

Fernando Rivas-Manzaneque, Jorge Sierra-Acosta, Adrian Penate-Sanchez, Francesc Moreno-Noguer, Angela Ribeiro

CVPR 2023poster

While original Neural Radiance Fields (NeRF) have shown impressive results in modeling the appearance of a scene with compact MLP architectures, they are not able to achieve real-time rendering. This has been recently addressed by either baking the outputs of NeRF into a data structure or arranging…

Cited by 6 Source ↗PDF ↗Scholar ↗

2017

Depth-aware convolutional neural networks for accurate 3D pose estimation in RGB-D images

Lorenzo Porzi, Adrian Penate-Sanchez, Elisa Ricci, Francesc Moreno-Noguer

IROS 2017poster

Most recent approaches to 3D pose estimation from RGB-D images address the problem in a two-stage pipeline. First, they learn a classifier-typically a random forest-to predict the position of each input pixel on the object surface. These estimates are then used to define an energy function that is m…

Cited by 16 Source ↗Scholar ↗

2015

A Dynamic Programming Approach for Fast and Robust Object Pose Recognition From Range Images

Christopher Zach, Adrian Penate-Sanchez, Minh-Tri Pham

CVPR 2015poster

Joint object recognition and pose estimation solely from range images is an important task e.g. in robotics applications and in automated manufacturing environments. The lack of color information and limitations of current commodity depth sensors make this task a challenging computer vision problem,…

Cited by 59 Source ↗PDF ↗Scholar ↗