Amir Hamza — accepted AI-conference papers

2025

Distilling 3D distinctive local descriptors for 6D pose estimation

Amir Hamza, Andrea Caraffa, Davide Boscaini, Fabio Poiesi

IROS 2025

Three-dimensional local descriptors are crucial for encoding geometric surface properties, making them essential for various point cloud understanding tasks. Among these descriptors, GeDi has demonstrated strong zero-shot 6D pose estimation capabilities but remains computationally impractical for re

Cited by 2 Source ↗code ↗Scholar ↗

2024

FreeZe: Training-free zero-shot 6D pose estimation with geometric and vision foundation models

Andrea Caraffa*, Davide Boscaini, Amir Hamza, Fabio Poiesi

ECCV 2024poster

"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 additional task-specific supervision provided by large-scale, photo-realistic synthetic datasets. However, their performance…

Cited by 17 Source ↗PDF ↗code ↗Scholar ↗