Search Results for author:
Found 15 papers, 5 papers with code
EpipolarNVS: leveraging on Epipolar geometry for single-image Novel View Synthesis
Novel-view synthesis (NVS) can be tackled through different approaches, depending on the general setting: a single source image to a short video sequence, exact or noisy camera pose information, 3D-based information such as point clouds etc.
Pruning-based Topology Refinement of 3D Mesh using a 2D Alpha Mask
Image-based 3D reconstruction has increasingly stunning results over the past few years with the latest improvements in computer vision and graphics.
Self-Improving SLAM in Dynamic Environments: Learning When to Mask
Thus, we propose a novel SLAM that learns when masking objects improves its performance in dynamic scenarios.
Trustworthiness of Laser-Induced Breakdown Spectroscopy Predictions via Simulation-based Synthetic Data Augmentation and Multitask Learning
The procedure is an end-to-end pipeline including the process of synthetic data augmentation, the construction of a suitable robust, homoscedastic, deep learning model, and the validation of its predictions.
Selective Multiple Power Iteration: from Tensor PCA to gradient-based exploration of landscapes
Various numerical simulations for $k=3$ in the conventionally considered range $n \leq 1000$ show that the experimental performances of SMPI improve drastically upon existent algorithms and becomes comparable to the theoretical optimal recovery.
HyperPCA: a Powerful Tool to Extract Elemental Maps from Noisy Data Obtained in LIBS Mapping of Materials
Laser-induced breakdown spectroscopy is a preferred technique for fast and direct multi-elemental mapping of samples under ambient pressure, without any limitation on the targeted element.
A Multiple-View Geometric Model for Specularity Prediction on General Curved Surfaces
Specularity prediction using our new model is tested against the most recent JOLIMAS version on both synthetic and real sequences with objects of various general shapes.
Deep multi-task mining Calabi-Yau four-folds
We continue earlier efforts in computing the dimensions of tangent space cohomologies of Calabi-Yau manifolds using deep learning.
A new framework for tensor PCA based on trace invariants
We consider the Principal Component Analysis (PCA) problem for tensors $T \in (\mathbb{R}^n)^{\otimes k}$ of large dimension $n$ and of arbitrary order $k\geq 3$.
Learning to Segment Dynamic Objects using SLAM Outliers
Our dataset includes consensus inversions, i. e., situations where the SLAM uses more features on dynamic objects that on the static background.
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
This paper shows that deep learning (DL) representations of data produced by generative adversarial nets (GANs) are random vectors which fall within the class of so-called \textit{concentrated} random vectors.
A Kernel Random Matrix-Based Approach for Sparse PCA
In this paper, we present a random matrix approach to recover sparse principal components from n p-dimensional vectors.
Deep Multi-class Adversarial Specularity Removal
We propose a novel learning approach, in the form of a fully-convolutional neural network (CNN), which automatically and consistently removes specular highlights from a single image by generating its diffuse component.
Generative Collaborative Networks for Single Image Super-Resolution
In this paper, we present a general framework named \textit{Generative Collaborative Networks} (GCN), where the idea consists in optimizing the \textit{generator} (the mapping of interest) in the feature space of a \textit{features extractor} network.
Learning More Universal Representations for Transfer-Learning
We also propose a unified framework of the methods based on the diversifying of the training problem.
