Occlusions in robotic bin picking compromise accurate and reliable grasp planning. We present ViTA-Seg, a class-agnostic Vision Transformer framework for real-time amodal segmentation that leverages global attention to recover complete object masks, including hidden regions. We proposte two architectures: a) Single-Head for amodal mask prediction; b) Dual-Head for amodal and occluded mask prediction. We also introduce ViTA-SimData, a photo-realistic synthetic dataset tailored to industrial bin-picking scenario. Extensive experiments on two amodal benchmarks, COOCA and KINS, demonstrate that ViTA-Seg Dual Head achieves strong amodal and occlusion segmentation accuracy with computational efficiency, enabling robust, real-time robotic manipulation.
@article{arxiv.2512.09510,
title = {ViTA-Seg: Vision Transformer for Amodal Segmentation in Robotics},
author = {Donato Caramia and Florian T. Pokorny and Giuseppe Triggiani and Denis Ruffino and David Naso and Paolo Roberto Massenio},
journal= {arXiv preprint arXiv:2512.09510},
year = {2025}
}