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arXiv · 2512.09510

ViTA-Seg: Vision Transformer for Amodal Segmentation in Robotics

Donato Caramia·Florian T. Pokorny·Giuseppe Triggiani·Denis Ruffino·David Naso·Paolo Roberto Massenio
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Abstract

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.

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Donato Caramia, Florian T. Pokorny, Giuseppe Triggiani, Denis Ruffino, David Naso, Paolo Roberto Massenio. 2025-12-10. ViTA-Seg: Vision Transformer for Amodal Segmentation in Robotics. https://arxiv.org/abs/2512.09510

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