Functionality understanding and segmentation in 3D scenes

2024-11-25

arXiv.org

AI Summary

This work addresses the problem of natural-language-driven functional object localization and segmentation in 3D scenes—e.g., interpreting “turn on the ceiling light” to identify an unmentioned switch. We propose Fun3DU, the first training-free framework for functional understanding in 3D. Methodologically, it introduces an end-to-end paradigm integrating chain-of-thought language reasoning, multi-view vision-language model (VLM)-based segmentation, and geometry-aware point cloud aggregation—requiring zero fine-tuning and relying solely on off-the-shelf pretrained large language models and VLMS. Its core innovation lies in explicitly modeling joint reasoning over semantic functional intent and spatial-geometric structure. Evaluated on the SceneFun3D benchmark, Fun3DU significantly outperforms existing open-vocabulary 3D segmentation methods, establishing the first generalizable, training-free, and functionally grounded approach to 3D semantic segmentation.

Technology Category

• Computer Vision: 3D Computer Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP
• 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: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Understanding functionalities in 3D scenes involves interpreting natural language descriptions to locate functional interactive objects, such as handles and buttons, in a 3D environment. Functionality understanding is highly challenging, as it requires both world knowledge to interpret language and spatial perception to identify fine-grained objects. For example, given a task like 'turn on the ceiling light', an embodied AI agent must infer that it needs to locate the light switch, even though the switch is not explicitly mentioned in the task description. To date, no dedicated methods have been developed for this problem. In this paper, we introduce Fun3DU, the first approach designed for functionality understanding in 3D scenes. Fun3DU uses a language model to parse the task description through Chain-of-Thought reasoning in order to identify the object of interest. The identified object is segmented across multiple views of the captured scene by using a vision and language model. The segmentation results from each view are lifted in 3D and aggregated into the point cloud using geometric information. Fun3DU is training-free, relying entirely on pre-trained models. We evaluate Fun3DU on SceneFun3D, the most recent and only dataset to benchmark this task, which comprises over 3000 task descriptions on 230 scenes. Our method significantly outperforms state-of-the-art open-vocabulary 3D segmentation approaches. Project page: https://tev-fbk.github.io/fun3du/

Problem

• Interpreting natural language to locate functional objects in 3D scenes
• Segmenting fine-grained interactive objects using vision and language models
• Training-free functionality understanding in 3D environments

Innovation

• Uses Chain-of-Thought reasoning for task parsing
• Lifts 2D segmentations into 3D point clouds
• Relies entirely on pre-trained models