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Alberto Remus

5 accepted papers

2026

InstantPose: Zero-Shot Instance-Level 6D Pose Estimation from a Single View

Francesco Di Felice, Alberto Remus, Stefano Gasperini, Benjamin Busam, Lionel Ott, Stefan Thalhammer +2
ICRA 2026poster

Object pose estimation using visual data is crucial for robotic interaction with the environment. Many existing instance-level methods are restricted by their requirements for 3D CAD models or multiple object views, which limits their flexibility and generalizability. Overcoming this limitation is c…

Cited by 0Source ↗Scholar ↗
2025

InstantPose: Zero-Shot Instance-Level 6D Pose Estimation From a Single View

Francesco Di Felice, Alberto Remus, Stefano Gasperini, Benjamin Busam, Lionel Ott, Stefan Thalhammer +2
RA-L 2025

Object pose estimation using visual data is crucial for robotic interaction with the environment. Many existing instance-level methods are restricted by their requirements for 3D CAD models or multiple object views, which limits their flexibility and generalizability. Overcoming this limitation is c

Cited by 4Source ↗Scholar ↗
2024

Zero123-6D: Zero-shot Novel View Synthesis for RGB Category-level 6D Pose Estimation

Francesco Di Felice, Alberto Remus, Stefano Gasperini, Benjamin Busam, Lionel Ott, Federico Tombari +2
IROS 2024

Estimating the pose of objects through vision is essential to make robotic platforms interact with the environment. Yet, it presents many challenges, often related to the lack of flexibility and generalizability of state-of-the-art solutions. Diffusion models are a cutting-edge neural architecture t

Cited by 13Source ↗Scholar ↗
2023

One-Shot Imitation Learning With Graph Neural Networks for Pick-and-Place Manipulation Tasks

Francesco Di Felice, Salvatore D'Avella, Alberto Remus, Paolo Tripicchio, Carlo Alberto Avizzano
RA-L 2023

The proposed work presents a framework based on Graph Neural Networks (GNN) that abstracts the task to be executed and directly allows the robot to learn task-specific rules from synthetic demonstrations given through imitation learning. A graph representation of the state space is considered to enc

Cited by 15Source ↗Scholar ↗
2023

i2c-net: Using Instance-Level Neural Networks for Monocular Category-Level 6D Pose Estimation

Alberto Remus, Salvatore D'Avella, Francesco Di Felice, Paolo Tripicchio, Carlo Alberto Avizzano
RA-L 2023

Object detection and pose estimation are strict requirements for many robotic grasping and manipulation applications to endow robots with the ability to grasp objects with different properties in cluttered scenes and with various lighting conditions. This work proposes the framework <italic xmlns:mm

Cited by 23Source ↗Scholar ↗