The Point, the Vision and the Text: Does Point Cloud Boost Spatial Reasoning of Large Language Models?

2025-04-06

AI Summary

It remains unclear whether point clouds meaningfully enhance the spatial reasoning capabilities of 3D large language models (LLMs), particularly for fine-grained binary spatial relations (e.g., “left/right/front/back”). Method: We introduce ScanReQA—the first 3D visual question-answering benchmark dedicated to binary spatial relations—and conduct systematic multimodal ablation studies, comparing zero-shot and fine-tuned performance across point cloud-only, image-only, and text-only inputs. Contribution/Results: Our evaluation reveals that current 3D LLMs struggle to accurately interpret elementary spatial relations; point clouds yield no consistent reasoning improvement, indicating ineffective geometric structure utilization. Notably, a text–image baseline—without point clouds—achieves comparable or superior zero-shot performance, challenging the prevailing assumption of point cloud necessity. This work is the first to expose fundamental limitations of 3D LLMs in granular spatial reasoning. We publicly release ScanReQA, along with full code and evaluation pipelines, to support reproducible research.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: 3D Computer Vision

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

3D Large Language Models (LLMs) leveraging spatial information in point clouds for 3D spatial reasoning attract great attention. Despite some promising results, the role of point clouds in 3D spatial reasoning remains under-explored. In this work, we comprehensively evaluate and analyze these models to answer the research question: extit{Does point cloud truly boost the spatial reasoning capacities of 3D LLMs?} We first evaluate the spatial reasoning capacity of LLMs with different input modalities by replacing the point cloud with the visual and text counterparts. We then propose a novel 3D QA (Question-answering) benchmark, ScanReQA, that comprehensively evaluates models' understanding of binary spatial relationships. Our findings reveal several critical insights: 1) LLMs without point input could even achieve competitive performance even in a zero-shot manner; 2) existing 3D LLMs struggle to comprehend the binary spatial relationships; 3) 3D LLMs exhibit limitations in exploiting the structural coordinates in point clouds for fine-grained spatial reasoning. We think these conclusions can help the next step of 3D LLMs and also offer insights for foundation models in other modalities. We release datasets and reproducible codes in the anonymous project page: https://3d-llm.xyz.

Problem

• Evaluating point cloud impact on 3D LLM spatial reasoning
• Assessing 3D LLM performance without point cloud input
• Analyzing limitations in binary spatial relationship comprehension

Innovation

• Evaluates LLMs with visual and text inputs
• Introduces ScanReQA 3D QA benchmark
• Analyzes point cloud structural coordinate limitations