4 citations · 9 across the 8 of their papers we have counts for
10 papers
Bridging Human and Model Attention: Explainability Analysis of CNN, Mamba, and ViT Architectures with Gaze-Based Validation
Marco Stefani, Edoardo Parpaiola, Matteo Bino +5
High-risk applications of deep learning require precise characterization of model decision-making. We conduct a systematic explainability evaluation across Convolutional Neural Net…
TravKAN: Fast and Interpretable Nonlinear Traversability Analysis with Kolmogorov-Arnold Networks
Daniel Fusaro, Simone Mosco, Wanmeng Li +1
Traversability analysis is a fundamental capability for autonomous mobile robots operating in unstructured environments. While modern machine learning approaches such as deep neura…
Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentation
Simone Mosco, Daniel Fusaro, Alberto Pretto
Understanding the surrounding environment is fundamental in autonomous driving and robotic perception. Distinguishing between known classes and previously unseen objects is crucial…
DPGLA: Bridging the Gap between Synthetic and Real Data for Unsupervised Domain Adaptation in 3D LiDAR Semantic Segmentation
Wanmeng Li, Simone Mosco, Daniel Fusaro +1
Annotating real-world LiDAR point clouds for use in intelligent autonomous systems is costly. To overcome this limitation, self-training-based Unsupervised Domain Adaptation (UDA)…
Point-Plane Projections for Accurate LiDAR Semantic Segmentation in Small Data Scenarios
Simone Mosco, Daniel Fusaro, Wanmeng Li +2
LiDAR point cloud semantic segmentation is essential for interpreting 3D environments in applications such as autonomous driving and robotics. Recent methods achieve strong perform…
Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Colored Point Clouds
Daniel Fusaro, Federico Magistri, Jens Behley +2
Accurate and consistent fruit monitoring over time is a key step toward automated agricultural production systems. However, this task is inherently difficult due to variations in f…
Exploiting Local Features and Range Images for Small Data Real-Time Point Cloud Semantic Segmentation
Daniel Fusaro, Simone Mosco, Emanuele Menegatti +1
Semantic segmentation of point clouds is an essential task for understanding the environment in autonomous driving and robotics. Recent range-based works achieve real-time efficien…
A Sonar-based AUV Positioning System for Underwater Environments with Low Infrastructure Density
Emilio Olivastri, Daniel Fusaro, Wanmeng Li +2
The increasing demand for underwater vehicles highlights the necessity for robust localization solutions in inspection missions. In this work, we present a novel real-time sonar-ba…
Improving Generalization of Synthetically Trained Sonar Image Descriptors for Underwater Place Recognition
Ivano Donadi, Emilio Olivastri, Daniel Fusaro +3
Autonomous navigation in underwater environments presents challenges due to factors such as light absorption and water turbidity, limiting the effectiveness of optical sensors. Son…
Pushing the Limits of Learning-based Traversability Analysis for Autonomous Driving on CPU
Daniel Fusaro, Emilio Olivastri, Daniele Evangelista +3
Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation. This pap…