Zobrazeno 1 - 10
of 510
pro vyhledávání: '"MARTINS, RENATO P."'
This paper presents a dense depth estimation approach from light-field (LF) images that is able to compensate for strong rolling shutter (RS) effects. Our method estimates RS compensated views and dense RS compensated disparity maps. We present a two
Externí odkaz:
http://arxiv.org/abs/2412.03518
Autor:
Cadar, Felipe, Potje, Guilherme, Martins, Renato, Demonceaux, Cédric, Nascimento, Erickson R.
Visual correspondence is a crucial step in key computer vision tasks, including camera localization, image registration, and structure from motion. The most effective techniques for matching keypoints currently involve using learned sparse or dense m
Externí odkaz:
http://arxiv.org/abs/2410.09533
We introduce a lightweight and accurate architecture for resource-efficient visual correspondence. Our method, dubbed XFeat (Accelerated Features), revisits fundamental design choices in convolutional neural networks for detecting, extracting, and ma
Externí odkaz:
http://arxiv.org/abs/2404.19174
Polarization information of the light can provide rich cues for computer vision and scene understanding tasks, such as the type of material, pose, and shape of the objects. With the advent of new and cheap polarimetric sensors, this imaging modality
Externí odkaz:
http://arxiv.org/abs/2312.14697
In this paper, we propose an approach to address the problem of 3D reconstruction of scenes from a single image captured by a light-field camera equipped with a rolling shutter sensor. Our method leverages the 3D information cues present in the light
Externí odkaz:
http://arxiv.org/abs/2311.01292
Autor:
Cadar, Felipe, Melo, Welerson, Kanagasabapathi, Vaishnavi, Potje, Guilherme, Martins, Renato, Nascimento, Erickson R.
Publikováno v:
Pattern Recognition Letters 2023
We propose a novel learned keypoint detection method to increase the number of correct matches for the task of non-rigid image correspondence. By leveraging true correspondences acquired by matching annotated image pairs with a specified descriptor e
Externí odkaz:
http://arxiv.org/abs/2309.00434
Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval. The core assumption of most methods is that images undergo affine transformations, disregarding more complicated eff
Externí odkaz:
http://arxiv.org/abs/2304.00583
Understanding when an abstract complex curve of given genus comes equipped with a map of fixed degree to a projective space of fixed dimension is a foundational question; and Brill--Noether theory addresses this question via linear series, which alge
Externí odkaz:
http://arxiv.org/abs/2302.13993
We present a novel learned keypoint detection method designed to maximize the number of correct matches for the task of non-rigid image correspondence. Our training framework uses true correspondences, obtained by matching annotated image pairs with
Externí odkaz:
http://arxiv.org/abs/2212.09589
In a previous paper, the first three authors formulated a precise conjecture about the dimension of the {\it generalized Severi variety} $M^n_{d,g; {\rm S}, {\bf k}}$ of degree-$d$ holomorphic maps $\mathbb{P}^1 \rightarrow \mathbb{P}^n$ whose images
Externí odkaz:
http://arxiv.org/abs/2211.09874