FirePlace: Geometric Refinements of LLM Common Sense Reasoning for 3D Object Placement

2025-03-06

AI Summary

This work addresses the problem of precise object placement in 3D scenes. Existing approaches suffer from limited geometric detail extraction, inadequate constraint modeling, and poor adherence to commonsense knowledge. To bridge this gap, we propose a novel method that synergistically integrates multimodal large language models (MLLMs) for semantic understanding with explicit 3D geometric reasoning. Our approach is the first to employ MLLMs end-to-end for geometric detail parsing, explicit geometric constraint formulation, and constraint solving. Furthermore, we introduce a commonsense-driven optimization pruning mechanism to align high-level semantics with low-level geometric feasibility. Experiments on complex real-world scenes demonstrate substantial improvements in both semantic plausibility and geometric validity of placements. Quantitative and qualitative evaluations show that our method consistently outperforms current state-of-the-art approaches across multiple metrics.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Scene generation with 3D assets presents a complex challenge, requiring both high-level semantic understanding and low-level geometric reasoning. While Multimodal Large Language Models (MLLMs) excel at semantic tasks, their application to 3D scene generation is hindered by their limited grounding on 3D geometry. In this paper, we investigate how to best work with MLLMs in an object placement task. Towards this goal, we introduce a novel framework, FirePlace, that applies existing MLLMs in (1) 3D geometric reasoning and the extraction of relevant geometric details from the 3D scene, (2) constructing and solving geometric constraints on the extracted low-level geometry, and (3) pruning for final placements that conform to common sense. By combining geometric reasoning with real-world understanding of MLLMs, our method can propose object placements that satisfy both geometric constraints as well as high-level semantic common-sense considerations. Our experiments show that these capabilities allow our method to place objects more effectively in complex scenes with intricate geometry, surpassing the quality of prior work.

Problem

• Enhance 3D object placement using MLLMs
• Integrate geometric reasoning with semantic understanding
• Improve scene generation quality with geometric constraints

Innovation

• Integrates MLLMs with 3D geometric reasoning
• Constructs and solves geometric constraints
• Prunes placements for common sense conformity