most citedA Multimodal Hybrid Late-Cascade Fusion Network for Enhanced 3D Object Detection

8 citations · 12 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2026

MiCaDe: Online LiDAR-Camera Miscalibration Change Detection for Autonomous Driving

Carlo Sgaravatti, Riccardo Pieroni, Matteo Corno +3

Reliable multi-sensor perception in autonomous vehicles requires accurate calibration between cameras and LiDAR. However, calibration drift is often unavoidable in long-term deploy…

cs.CV2026

DenseTwins: Dense Modality Decoupling for Multimodal Self-Supervision

Carlo Sgaravatti, Daniele Kopyshevskiy, Davide Secco +1

Multimodal perception is central to many computer vision applications, including robotics and autonomous driving, where different sensing modalities provide complementary cues. Sel…

cs.CV2026★ 4 cited

LCF3D: A Robust and Real-Time Late-Cascade Fusion Framework for 3D Object Detection in Autonomous Driving

Carlo Sgaravatti, Riccardo Pieroni, Matteo Corno +3

Accurately localizing 3D objects like pedestrians, cyclists, and other vehicles is essential in Autonomous Driving. To ensure high detection performance, Autonomous Vehicles comple…

cs.CV2026

Promptable Foundation Models for SAR Remote Sensing: Adapting the Segment Anything Model for Snow Avalanche Segmentation

Riccardo Gelato, Carlo Sgaravatti, Jakob Grahn +2

Remote sensing solutions for avalanche segmentation and mapping are key to supporting risk forecasting and mitigation in mountain regions. Synthetic Aperture Radar (SAR) imagery fr…

cs.CV2025★ 8 cited

A Multimodal Hybrid Late-Cascade Fusion Network for Enhanced 3D Object Detection

Carlo Sgaravatti, Roberto Basla, Riccardo Pieroni +4

We present a new way to detect 3D objects from multimodal inputs, leveraging both LiDAR and RGB cameras in a hybrid late-cascade scheme, that combines an RGB detection network and…