94 citations · 99 across the 21 of their papers we have counts for
24 papers
Prior-free relative 6D pose estimation of multiple object instances
Behdad Khodabandehloo, Andrea Caraffa, Davide Boscaini +1
Object 6D pose estimation formulations have progressively reduced reliance on object-specific priors, evolving from explicit 3D models to multi-view object captures to single refer…
Foundational feature fusion for conditional flow matching in 6D pose estimation
Amir Hamza, Davide Boscaini, Fabio Poiesi
Conditional flow matching has enabled a step forward in object 6D pose estimation, achieving state-of-the-art performance by progressively denoising and registering object represen…
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…
Generative 6D Pose Estimation via Conditional Flow Matching
Amir Hamza, Davide Boscaini, Weihang Li +2
Existing methods for instance-level 6D pose estimation typically rely on neural networks that either directly regress the pose in or estimate it indirectly via loc…
AI-driven visual monitoring of industrial assembly tasks
Mattia Nardon, Stefano Messelodi, Antonio Granata +3
Visual monitoring of industrial assembly tasks is critical for preventing equipment damage due to procedural errors and ensuring worker safety. Although commercial solutions exist,…
An analysis of vision-language models for fabric retrieval
Francesco Giuliari, Asif Khan Pattan, Mohamed Lamine Mekhalfi +1
Effective cross-modal retrieval is essential for applications like information retrieval and recommendation systems, particularly in specialized domains such as manufacturing, wher…
Accurate and efficient zero-shot 6D pose estimation with frozen foundation models
Andrea Caraffa, Davide Boscaini, Fabio Poiesi
Estimating the 6D pose of objects from RGBD data is a fundamental problem in computer vision, with applications in robotics and augmented reality. A key challenge is achieving gene…
CHIP: A multi-sensor dataset for 6D pose estimation of chairs in industrial settings
Mattia Nardon, Mikel Mujika Agirre, Ander González Tomé +7
Accurate 6D pose estimation of complex objects in 3D environments is essential for effective robotic manipulation. Yet, existing benchmarks fall short in evaluating 6D pose estimat…
Distilling 3D distinctive local descriptors for 6D pose estimation
Amir Hamza, Andrea Caraffa, Davide Boscaini +1
Three-dimensional local descriptors are crucial for encoding geometric surface properties, making them essential for various point cloud understanding tasks. Among these descriptor…
3D Part Segmentation via Geometric Aggregation of 2D Visual Features
Marco Garosi, Riccardo Tedoldi, Davide Boscaini +3
Supervised 3D part segmentation models are tailored for a fixed set of objects and parts, limiting their transferability to open-set, real-world scenarios. Recent works have explor…
Leveraging Confident Image Regions for Source-Free Domain-Adaptive Object Detection
Mohamed Lamine Mekhalfi, Davide Boscaini, Fabio Poiesi
Source-free domain-adaptive object detection is an interesting but scarcely addressed topic. It aims at adapting a source-pretrained detector to a distinct target domain without re…
Functionality understanding and segmentation in 3D scenes
Jaime Corsetti, Francesco Giuliari, Alice Fasoli +2
Understanding functionalities in 3D scenes involves interpreting natural language descriptions to locate functional interactive objects, such as handles and buttons, in a 3D enviro…
Exploring Fine-grained Retail Product Discrimination with Zero-shot Object Classification Using Vision-Language Models
Anil Osman Tur, Alessandro Conti, Cigdem Beyan +5
In smart retail applications, the large number of products and their frequent turnover necessitate reliable zero-shot object classification methods. The zero-shot assumption is ess…
Wild Berry image dataset collected in Finnish forests and peatlands using drones
Luigi Riz, Sergio Povoli, Andrea Caraffa +13
Berry picking has long-standing traditions in Finland, yet it is challenging and can potentially be dangerous. The integration of drones equipped with advanced imaging techniques r…
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…
Learning general and distinctive 3D local deep descriptors for point cloud registration
Fabio Poiesi, Davide Boscaini
An effective 3D descriptor should be invariant to different geometric transformations, such as scale and rotation, robust to occlusions and clutter, and capable of generalising to…
Distinctive 3D local deep descriptors
Fabio Poiesi, Davide Boscaini
We present a simple but yet effective method for learning distinctive 3D local deep descriptors (DIPs) that can be used to register point clouds without requiring an initial alignm…
Novel-View Human Action Synthesis
Mohamed Ilyes Lakhal, Davide Boscaini, Fabio Poiesi +2
Novel-View Human Action Synthesis aims to synthesize the movement of a body from a virtual viewpoint, given a video from a real viewpoint. We present a novel 3D reasoning to synthe…
3D Shape Segmentation with Geometric Deep Learning
Davide Boscaini, Fabio Poiesi
The semantic segmentation of 3D shapes with a high-density of vertices could be impractical due to large memory requirements. To make this problem computationally tractable, we pro…