Do 3D Large Language Models Really Understand 3D Spatial Relationships?
arXiv:2603.23523v1 Announce Type: new Abstract: Recent 3D Large-Language Models (3D-LLMs) claim to understand 3D worlds, especially spatial relationships among objects. Yet, we find that simply fine-tuning a language model on text-only question-answer pairs can perform comparably or even surpass these methods on the SQA3D benchmark without using any 3D input. This indicates that the SQA3D benchmark may not be able to detect if the model exploits textual shortcuts rather than engages in 3D-aware reasoning. To address this issue, we introduce Real-3DQA, a more rigorous evaluation benchmark that filters out easy-to-guess questions and introduces a structured taxonomy to assess various aspects of 3D reasoning. Experiments on Real-3DQA confirm that existing 3D-LLMs struggle with spatial relationships once simple cues are removed. We further propose a 3D-reweighted training objective that guides model to rely more on 3D visual clues, substantially enhancing 3D-LLMs performance in spatial re
arXiv:2603.23523v1 Announce Type: new Abstract: Recent 3D Large-Language Models (3D-LLMs) claim to understand 3D worlds, especially spatial relationships among objects. Yet, we find that simply fine-tuning a language model on text-only question-answer pairs can perform comparably or even surpass these methods on the SQA3D benchmark without using any 3D input. This indicates that the SQA3D benchmark may not be able to detect if the model exploits textual shortcuts rather than engages in 3D-aware reasoning. To address this issue, we introduce Real-3DQA, a more rigorous evaluation benchmark that filters out easy-to-guess questions and introduces a structured taxonomy to assess various aspects of 3D reasoning. Experiments on Real-3DQA confirm that existing 3D-LLMs struggle with spatial relationships once simple cues are removed. We further propose a 3D-reweighted training objective that guides model to rely more on 3D visual clues, substantially enhancing 3D-LLMs performance in spatial reasoning tasks. Our findings underscore the need for robust benchmarks and tailored training strategies to advance genuine 3D vision-language understanding. Project page: https://real-3dqa.github.io/.
Executive Summary
This article challenges the notion that recent 3D Large-Language Models (3D-LLMs) truly understand 3D spatial relationships. By fine-tuning a language model on text-only question-answer pairs, the authors demonstrate comparable or superior performance on the SQA3D benchmark without using any 3D input. This suggests that existing 3D-LLMs may exploit textual shortcuts rather than genuine 3D-aware reasoning. To address this issue, the authors propose Real-3DQA, a more rigorous evaluation benchmark that assesses various aspects of 3D reasoning. The study highlights the need for robust benchmarks and tailored training strategies to advance genuine 3D vision-language understanding. The findings have significant implications for the development of effective 3D-LLMs and their applications in areas such as robotics, computer vision, and natural language processing.
Key Points
- ▸ Existing 3D-LLMs may not truly understand 3D spatial relationships
- ▸ Textual shortcuts can be exploited to achieve comparable performance
- ▸ Real-3DQA is a more rigorous evaluation benchmark for 3D reasoning
Merits
Strength in proposing a more rigorous evaluation benchmark
The Real-3DQA benchmark provides a more comprehensive assessment of 3D reasoning capabilities, helping to identify genuine 3D-aware models.
Demerits
Limitation in fine-tuning a language model on text-only data
The approach may not be applicable to real-world scenarios where 3D input is necessary.
Expert Commentary
The article highlights a critical issue in the field of 3D-LLMs: the potential for models to exploit textual shortcuts rather than genuine 3D-aware reasoning. This suggests that existing models may not truly understand 3D spatial relationships, which is a crucial aspect of many applications. The proposed Real-3DQA benchmark is a significant contribution to the field, providing a more rigorous evaluation of 3D reasoning capabilities. However, the approach of fine-tuning a language model on text-only data may not be applicable to real-world scenarios where 3D input is necessary. Further research is needed to develop effective 3D-LLMs that can truly understand 3D spatial relationships.
Recommendations
- ✓ Develop more robust benchmarks and tailored training strategies to advance genuine 3D vision-language understanding
- ✓ Investigate the potential applications of 3D-LLMs in areas such as robotics, computer vision, and natural language processing
Sources
Original: arXiv - cs.CL
