3 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning
Mohamed Lamine Mekhalfi, Paul Chippendale, Fabio Poiesi +3
This research investigates the application of computer vision for rapid, accurate, and non-invasive food quality assessment, focusing on the novel challenge of real-time raspberry…
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…
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 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…