ViTA-Seg: Vision Transformer for Amodal Segmentation in Robotics

• URL: http://arxiv.org/abs/2512.09510v1
• Date: Wed, 10 Dec 2025 10:34:43 GMT
• Title: ViTA-Seg: Vision Transformer for Amodal Segmentation in Robotics
• Authors: Donato Caramia, Florian T. Pokorny, Giuseppe Triggiani, Denis Ruffino, David Naso, Paolo Roberto Massenio,
• Abstract summary: We present ViTA-Seg, a class-agnostic Vision Transformer framework for real-time amodal segmentation.<n>We also introduce ViTA-SimData, a photo-realistic synthetic dataset tailored to industrial bin-picking scenario.
• Score: 6.117506436557094
• License: http://creativecommons.org/licenses/by/4.0/
• 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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