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20242026
most citedA Multimodal Hybrid Late-Cascade Fusion Network for Enhanced 3D Object Detection

8 citations · 18 across the 8 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

Superquadric Primitive Decomposition of 3D point clouds via Geometric-Aware Inlier Refinement

Alessandro Rinaldi, Edoardo Tedesco, Andrea Ferraris +5

The decomposition of 3D point clouds into interpretable geometric primitives remains a longstanding challenge in Computer Vision and Computer Graphics. Among the available represen…

cs.CV2026

Cell Phantom Video Generation in Elliptical Fourier Descriptor Domain

Francesco Benedetto, Roberto Basla, Luca Magri +1

Training Deep Neural Networks for tracking individual cells in biomedical videos requires a large amount of annotated data. The annotation of videos for cell tracking is very time…

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.CV2025

One target to align them all: LiDAR, RGB and event cameras extrinsic calibration for Autonomous Driving

Andrea Bertogalli, Giacomo Boracchi, Luca Magri

We present a novel multi-modal extrinsic calibration framework designed to simultaneously estimate the relative poses between event cameras, LiDARs, and RGB cameras, with particula…

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…

cs.CV2025

Temporal-consistent CAMs for Weakly Supervised Video Segmentation in Waste Sorting

Andrea Marelli, Luca Magri, Federica Arrigoni +1

In industrial settings, weakly supervised (WS) methods are usually preferred over their fully supervised (FS) counterparts as they do not require costly manual annotations. Unfortu…

cs.CV2024

Minimal Perspective Autocalibration

Andrea Porfiri Dal Cin, Timothy Duff, Luca Magri +1

We introduce a new family of minimal problems for reconstruction from multiple views. Our primary focus is a novel approach to autocalibration, a long-standing problem in computer…