3 citations · 6 across the 10 of their papers we have counts for
10 papers
xperception -- Making Robotic Grasping Easier
Matteo Bortolon, Andrea Caraffa, Alice Fasoli +1
The transition toward high-mix low-volume manufacturing demands flexibility in robotic manipulation. However, conventional vision systems remain a bottleneck, requiring extensive d…
Obstruction reasoning for robotic grasping
Runyu Jiao, Matteo Bortolon, Francesco Giuliari +5
Successful robotic grasping in cluttered environments not only requires a model to visually ground a target object but also to reason about obstructions that must be cleared before…
GRASPLAT: Enabling dexterous grasping through novel view synthesis
Matteo Bortolon, Nuno Ferreira Duarte, Plinio Moreno +3
Achieving dexterous robotic grasping with multi-fingered hands remains a significant challenge. While existing methods rely on complete 3D scans to predict grasp poses, these appro…
Free-form language-based robotic reasoning and grasping
Runyu Jiao, Alice Fasoli, Francesco Giuliari +5
Performing robotic grasping from a cluttered bin based on human instructions is a challenging task, as it requires understanding both the nuances of free-form language and the spat…
6DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Model
Matteo Bortolon, Theodore Tsesmelis, Stuart James +2
We propose 6DGS to estimate the camera pose of a target RGB image given a 3D Gaussian Splatting (3DGS) model representing the scene. 6DGS avoids the iterative process typical of an…
IFFNeRF: Initialisation Free and Fast 6DoF pose estimation from a single image and a NeRF model
Matteo Bortolon, Theodore Tsesmelis, Stuart James +2
We introduce IFFNeRF to estimate the six degrees-of-freedom (6DoF) camera pose of a given image, building on the Neural Radiance Fields (NeRF) formulation. IFFNeRF is specifically…
The MONET dataset: Multimodal drone thermal dataset recorded in rural scenarios
Luigi Riz, Andrea Caraffa, Matteo Bortolon +11
We present MONET, a new multimodal dataset captured using a thermal camera mounted on a drone that flew over rural areas, and recorded human and vehicle activities. We captured MON…
VM-NeRF: Tackling Sparsity in NeRF with View Morphing
Matteo Bortolon, Alessio Del Bue, Fabio Poiesi
NeRF aims to learn a continuous neural scene representation by using a finite set of input images taken from various viewpoints. A well-known limitation of NeRF methods is their re…
Multi-view data capture for dynamic object reconstruction using handheld augmented reality mobiles
M. Bortolon, L. Bazzanella, F. Poiesi
We propose a system to capture nearly-synchronous frame streams from multiple and moving handheld mobiles that is suitable for dynamic object 3D reconstruction. Each mobile execute…
Multi-view data capture using edge-synchronised mobiles
Matteo Bortolon, Paul Chippendale, Stefano Messelodi +1
Multi-view data capture permits free-viewpoint video (FVV) content creation. To this end, several users must capture video streams, calibrated in both time and pose, framing the sa…