3D-TAFS: A Training-free Framework for 3D Affordance Segmentation

2024-09-16

AI Summary

This paper addresses the challenge of mapping natural-language instructions to 3D object affordance regions. We propose the first zero-shot 3D affordance segmentation framework. Methodologically, it integrates large vision-language models with dedicated 3D vision networks, leveraging point cloud–image cross-modal alignment and prompt-driven segmentation to directly localize semantic instructions onto 3D affordance regions. Our key contributions are: (1) the introduction of IndoorAfford-Bench, the first large-scale indoor interaction benchmark for affordance understanding; (2) a fine-tuning-free, semantics-driven 3D affordance segmentation paradigm; and (3) seamless 2D/3D multimodal fusion. Evaluated on IndoorAfford-Bench, our method significantly outperforms existing approaches, achieving superior accuracy, generalization, and zero-shot adaptability in complex indoor human–robot interaction scenarios.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Translating high-level linguistic instructions into precise robotic actions in the physical world remains challenging, particularly when considering the feasibility of interacting with 3D objects. In this paper, we introduce 3D-TAFS, a novel training-free multimodal framework for 3D affordance segmentation. To facilitate a comprehensive evaluation of such frameworks, we present IndoorAfford-Bench, a large-scale benchmark containing 9,248 images spanning 20 diverse indoor scenes across 6 areas, supporting standardized interaction queries. In particular, our framework integrates a large multimodal model with a specialized 3D vision network, enabling a seamless fusion of 2D and 3D visual understanding with language comprehension. Extensive experiments on IndoorAfford-Bench validate the proposed 3D-TAFS's capability in handling interactive 3D affordance segmentation tasks across diverse settings, showcasing competitive performance across various metrics. Our results highlight 3D-TAFS's potential for enhancing human-robot interaction based on affordance understanding in complex indoor environments, advancing the development of more intuitive and efficient robotic frameworks for real-world applications.

Problem

• Translating linguistic instructions into robotic actions
• Segmenting 3D affordances without training
• Enhancing human-robot interaction in indoor environments

Innovation

• Training-free multimodal framework for 3D segmentation
• Integrates large multimodal model with 3D vision
• Benchmark with 9,248 images across 20 scenes