169 citations · 307 across the 20 of their papers we have counts for
6 papers · 1 filter
FreeZe: Training-free zero-shot 6D pose estimation with geometric and vision foundation models
Andrea Caraffa, Davide Boscaini, Amir Hamza +1
Estimating the 6D pose of objects unseen during training is highly desirable yet challenging. Zero-shot object 6D pose estimation methods address this challenge by leveraging addit…
Open-vocabulary object 6D pose estimation
Jaime Corsetti, Davide Boscaini, Changjae Oh +2
We introduce the new setting of open-vocabulary object 6D pose estimation, in which a textual prompt is used to specify the object of interest. In contrast to existing approaches,…
Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object Detection
Mohamed L. Mekhalfi, Davide Boscaini, Fabio Poiesi
Unsupervised domain adaptation (UDA) plays a crucial role in object detection when adapting a source-trained detector to a target domain without annotated data. In this paper, we p…
PatchMixer: Rethinking network design to boost generalization for 3D point cloud understanding
Davide Boscaini, Fabio Poiesi
The recent trend in deep learning methods for 3D point cloud understanding is to propose increasingly sophisticated architectures either to better capture 3D geometries or by intro…
Revisiting Fully Convolutional Geometric Features for Object 6D Pose Estimation
Jaime Corsetti, Davide Boscaini, Fabio Poiesi
Recent works on 6D object pose estimation focus on learning keypoint correspondences between images and object models, and then determine the object pose through RANSAC-based algor…
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…