LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent (2309.12311v1)

Abstract: 3D visual grounding is a critical skill for household robots, enabling them to navigate, manipulate objects, and answer questions based on their environment. While existing approaches often rely on extensive labeled data or exhibit limitations in handling complex language queries, we propose LLM-Grounder, a novel zero-shot, open-vocabulary, LLM-based 3D visual grounding pipeline. LLM-Grounder utilizes an LLM to decompose complex natural language queries into semantic constituents and employs a visual grounding tool, such as OpenScene or LERF, to identify objects in a 3D scene. The LLM then evaluates the spatial and commonsense relations among the proposed objects to make a final grounding decision. Our method does not require any labeled training data and can generalize to novel 3D scenes and arbitrary text queries. We evaluate LLM-Grounder on the ScanRefer benchmark and demonstrate state-of-the-art zero-shot grounding accuracy. Our findings indicate that LLMs significantly improve the grounding capability, especially for complex language queries, making LLM-Grounder an effective approach for 3D vision-language tasks in robotics. Videos and interactive demos can be found on the project website https://chat-with-nerf.github.io/ .

LLM-Grounder: Open-Vocabulary 3D Visual Grounding with LLM as an Agent

The paper "LLM-Grounder: Open-Vocabulary 3D Visual Grounding with LLM as an Agent" introduces a novel approach addressing the zero-shot open-vocabulary 3D visual grounding problem by leveraging LLMs like GPT-4. This methodology integrates the powerful language comprehension and reasoning capabilities of LLMs with the visual recognition abilities of CLIP-based models, such as OpenScene and LERF.

The core objective of 3D visual grounding is to locate objects in a 3D scene using natural language queries. This task is pivotal for household robots, enabling them to perform complex tasks related to navigation, manipulation, and information retrieval in dynamic environments. Traditional methods, which require extensive labeled datasets or exhibit limitations in handling nuanced language queries, are often inadequate in zero-shot and open-vocabulary contexts.

Methodology

LLM-Grounder seeks to overcome these limitations by employing a three-step process managed by an LLM agent:

1. Query Decomposition: The LLM breaks down complex natural language queries into semantic components. This involves parsing the input into simpler constituent parts that describe object categories, attributes, landmarks, and spatial relations.
2. Tool-Orchestration and Interaction: Utilizing visual grounding tools like OpenScene and LERF, the LLM directs these tools to find candidate objects in a 3D scene. These tools, based on CLIP models, propose potential bounding boxes for the identified components. Despite their strengths, these models often treat text input as a "bag of words" without considering the semantic structure. LLM-Grounder addresses this by using the LLM to orchestrate these tools efficiently.
3. Spatial and Commonsense Reasoning: The LLM evaluates the proposed candidates using spatial and commonsense knowledge to make final grounding decisions. The agent can reason about spatial relationships and assess feedback from the visual grounders to determine the most contextually appropriate candidates.

Experimental Results

The authors evaluated their framework using the ScanRefer benchmark, a standard dataset for 3D visual grounding tasks that includes detailed natural language descriptions associated with objects in 3D scenes. The performance metrics used were [email protected] and [email protected], representing the proportion of correctly localized objects within specific IoU thresholds.

The results demonstrate that LLM-Grounder achieves state-of-the-art zero-shot grounding accuracy. Specifically, it improved grounding accuracy on ScanRefer from 4.4% to 6.9% ([email protected]) and from 0.3% to 1.6% ([email protected]) when integrated with LERF. When used with OpenScene, LLM-Grounder increased grounding accuracy from 13.0% to 17.1% ([email protected]) and made smaller improvements at higher IoU thresholds.

An important observation from the ablation studies is that the LLM agent's effectiveness increases with the complexity of the language query. However, its performance gains diminish in scenes with high visual complexity where instance disambiguation becomes challenging. The authors attribute this to the limitations of current LLMs in interpreting intricate visual cues.

Implications

From a practical standpoint, LLM-Grounder significantly extends the applicability of 3D visual grounding in real-world scenarios, particularly for robotic systems operating in diverse environments. By enabling zero-shot generalization, this approach circumvents the need for extensive labeled datasets, which are often costly and time-consuming to procure.

Theoretically, this framework illustrates the synergetic potential of combining advanced LLMs with visual grounding tools, enriching both domains. It highlights the advantages of leveraging LLMs not just as passive text processors but as active reasoning agents capable of complex task decomposition and tool orchestration.

Future Directions

Future research can explore enhancing the visual recognition capabilities to support more precise bounding box predictions, thus improving performance on higher IoU thresholds. Additionally, incorporating more sophisticated feedback loops and interactive learning paradigms between the LLM agent and visual tools could further refine spatial reasoning and instance disambiguation. Investigating the deployment of such systems in real-time robotics applications would also be a promising avenue, despite challenges related to computational cost and latency.

In conclusion, the paper "LLM-Grounder" presents a compelling strategy for open-vocabulary 3D visual grounding by effectively integrating LLMs with existing visual grounding techniques, setting a new standard for the field and opening multiple pathways for future advancements in AI-driven robotic systems.