Ges3ViG: Incorporating Pointing Gestures into Language-Based 3D Visual Grounding for Embodied Reference Understanding

• URL: http://arxiv.org/abs/2504.09623v1
• Date: Sun, 13 Apr 2025 15:43:06 GMT
• Title: Ges3ViG: Incorporating Pointing Gestures into Language-Based 3D Visual Grounding for Embodied Reference Understanding
• Authors: Atharv Mahesh Mane, Dulanga Weerakoon, Vigneshwaran Subbaraju, Sougata Sen, Sanjay E. Sarma, Archan Misra,
• Abstract summary: 3-Dimensional Embodied Reference Understanding (3D-ERU) combines a language description and an accompanying pointing gesture to identify the most relevant target object in a 3D scene.<n>We introduce a data augmentation framework-Imputer, and use it to curate a new benchmark dataset-ImputeRefer for 3D-ERU.<n>We also propose Ges3ViG, a novel model for 3D-ERU that achieves 30% improvement in accuracy as compared to other 3D-ERU models and 9% compared to other purely language-based 3D grounding models.
• Score: 5.568166420745467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3-Dimensional Embodied Reference Understanding (3D-ERU) combines a language description and an accompanying pointing gesture to identify the most relevant target object in a 3D scene. Although prior work has explored pure language-based 3D grounding, there has been limited exploration of 3D-ERU, which also incorporates human pointing gestures. To address this gap, we introduce a data augmentation framework-Imputer, and use it to curate a new benchmark dataset-ImputeRefer for 3D-ERU, by incorporating human pointing gestures into existing 3D scene datasets that only contain language instructions. We also propose Ges3ViG, a novel model for 3D-ERU that achieves ~30% improvement in accuracy as compared to other 3D-ERU models and ~9% compared to other purely language-based 3D grounding models. Our code and dataset are available at https://github.com/AtharvMane/Ges3ViG.

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