Search Results for author:
Found 9 papers, 7 papers with code
ReLight My NeRF: A Dataset for Novel View Synthesis and Relighting of Real World Objects
By leveraging the dataset, we perform an ablation study on the relighting capability of variants of the vanilla NeRF architecture and identify a lightweight architecture that can render novel views of an object under novel light conditions, which we use to establish a non-trivial baseline for the dataset.
NeRF-Supervised Deep Stereo
We introduce a novel framework for training deep stereo networks effortlessly and without any ground-truth.
ScanNeRF: a Scalable Benchmark for Neural Radiance Fields
In this paper, we propose the first-ever real benchmark thought for evaluating Neural Radiance Fields (NeRFs) and, in general, Neural Rendering (NR) frameworks.
The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization
We present Eyecandies, a novel synthetic dataset for unsupervised anomaly detection and localization.
Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial Settings
In this paper we investigate how to effectively deploy deep learning in practical industrial settings, such as robotic grasping applications.
Shooting Labels: 3D Semantic Labeling by Virtual Reality
Availability of a few, large-size, annotated datasets, like ImageNet, Pascal VOC and COCO, has lead deep learning to revolutionize computer vision research by achieving astonishing results in several vision tasks. We argue that new tools to facilitate generation of annotated datasets may help spreading data-driven AI throughout applications and domains.
Semi-Automatic Labeling for Deep Learning in Robotics
In this paper, we propose Augmented Reality Semi-automatic labeling (ARS), a semi-automatic method which leverages on moving a 2D camera by means of a robot, proving precise camera tracking, and an augmented reality pen to define initial object bounding box, to create large labeled datasets with minimal human intervention.
Let's take a Walk on Superpixels Graphs: Deformable Linear Objects Segmentation and Model Estimation
While robotic manipulation of rigid objects is quite straightforward, coping with deformable objects is an open issue.
SkiMap: An Efficient Mapping Framework for Robot Navigation
We present a novel mapping framework for robot navigation which features a multi-level querying system capable to obtain rapidly representations as diverse as a 3D voxel grid, a 2. 5D height map and a 2D occupancy grid.
