3DAxisPrompt: Promoting the 3D Grounding and Reasoning in GPT-4o

• URL: http://arxiv.org/abs/2503.13185v1
• Date: Mon, 17 Mar 2025 13:57:05 GMT
• Title: 3DAxisPrompt: Promoting the 3D Grounding and Reasoning in GPT-4o
• Authors: Dingning Liu, Cheng Wang, Peng Gao, Renrui Zhang, Xinzhu Ma, Yuan Meng, Zhihui Wang,
• Abstract summary: We introduce a novel visual prompting method, called 3DAxisPrompt, to elicit the 3D understanding capabilities of MLLMs in real-world scenes.<n>Our study reveals that MLLMs, with the help of 3DAxisPrompt, can effectively perceive an object's 3D position in real-world scenarios.
• Score: 39.453830972834254
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Large Language Models (MLLMs) exhibit impressive capabilities across a variety of tasks, especially when equipped with carefully designed visual prompts. However, existing studies primarily focus on logical reasoning and visual understanding, while the capability of MLLMs to operate effectively in 3D vision remains an ongoing area of exploration. In this paper, we introduce a novel visual prompting method, called 3DAxisPrompt, to elicit the 3D understanding capabilities of MLLMs in real-world scenes. More specifically, our method leverages the 3D coordinate axis and masks generated from the Segment Anything Model (SAM) to provide explicit geometric priors to MLLMs and then extend their impressive 2D grounding and reasoning ability to real-world 3D scenarios. Besides, we first provide a thorough investigation of the potential visual prompting formats and conclude our findings to reveal the potential and limits of 3D understanding capabilities in GPT-4o, as a representative of MLLMs. Finally, we build evaluation environments with four datasets, i.e., ScanRefer, ScanNet, FMB, and nuScene datasets, covering various 3D tasks. Based on this, we conduct extensive quantitative and qualitative experiments, which demonstrate the effectiveness of the proposed method. Overall, our study reveals that MLLMs, with the help of 3DAxisPrompt, can effectively perceive an object's 3D position in real-world scenarios. Nevertheless, a single prompt engineering approach does not consistently achieve the best outcomes for all 3D tasks. This study highlights the feasibility of leveraging MLLMs for 3D vision grounding/reasoning with prompt engineering techniques.

Related papers

• How to Enable LLM with 3D Capacity? A Survey of Spatial Reasoning in LLM [39.65493154187172]
  Large Language Models (LLMs) have been leveraged to enhance 3D understanding tasks, showing potential to surpass traditional computer vision methods. We propose a taxonomy that categorizes existing methods into three branches: image-based methods deriving 3D understanding from 2D visual data, point cloud-based methods working directly with 3D representations, and hybrid modality-based methods combining multiple data streams.
  arXiv  Detail & Related papers  (2025-04-08T08:11:39Z)
• Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding [19.382210260928776]
  Video-3D LLM treats 3D scenes as dynamic videos and incorporates 3D position encoding into these representations.<n>Our model achieves state-of-the-art performance on several 3D scene understanding benchmarks.
  arXiv  Detail & Related papers  (2024-11-30T14:28:53Z)
• LLMI3D: MLLM-based 3D Perception from a Single 2D Image [77.13869413871028]
  multimodal large language models (MLLMs) excel in general capacity but underperform in 3D tasks.<n>In this paper, we propose solutions for weak 3D local spatial object perception, poor text-based geometric numerical output, and inability to handle camera focal variations.<n>We employ parameter-efficient fine-tuning for a pre-trained MLLM and develop LLMI3D, a powerful 3D perception MLLM.
  arXiv  Detail & Related papers  (2024-08-14T10:00:16Z)
• When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models [113.18524940863841]
  This survey provides a comprehensive overview of the methodologies enabling large language models to process, understand, and generate 3D data. Our investigation spans various 3D data representations, from point clouds to Neural Radiance Fields (NeRFs) It examines their integration with LLMs for tasks such as 3D scene understanding, captioning, question-answering, and dialogue.
  arXiv  Detail & Related papers  (2024-05-16T16:59:58Z)
• 3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding [12.823274886850697]
  We introduce a novel and efficient prompt tuning paradigm, 3DMIT. This paradigm eliminates the alignment stage between 3D scenes and language and extends the instruction prompt with the 3D modality information. We evaluate the effectiveness of our method across diverse tasks in the 3D scene domain.
  arXiv  Detail & Related papers  (2024-01-06T12:20:18Z)
• M3DBench: Let's Instruct Large Models with Multi-modal 3D Prompts [30.571811801090224]
  We introduce a comprehensive 3D instructionfollowing dataset called M3DBench. It supports general multimodal instructions interleaved with text, images, 3D objects, and other visual prompts. It unifies diverse 3D tasks at both region and scene levels, covering a variety of fundamental abilities in real-world 3D environments.
  arXiv  Detail & Related papers  (2023-12-17T16:53:30Z)
• An Embodied Generalist Agent in 3D World [67.16935110789528]
  We introduce LEO, an embodied multi-modal generalist agent that excels in perceiving, grounding, reasoning, planning, and acting in the 3D world. We collect large-scale datasets comprising diverse object-level and scene-level tasks, which require considerable understanding of and interaction with the 3D world. Through extensive experiments, we demonstrate LEO's remarkable proficiency across a wide spectrum of tasks, including 3D captioning, question answering, embodied reasoning, navigation and manipulation.
  arXiv  Detail & Related papers  (2023-11-18T01:21:38Z)
• Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes [56.727745047799246]
  3D scene understanding has gained significant attention due to its wide range of applications. This paper presents Chat-3D, which combines the 3D visual perceptual ability of pre-trained 3D representations and the impressive reasoning and conversation capabilities of advanced LLMs.
  arXiv  Detail & Related papers  (2023-08-17T03:52:15Z)
• Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes [68.61199623705096]
  Training models to apply common-sense linguistic knowledge and visual concepts from 2D images to 3D scene understanding is a promising direction that researchers have only recently started to explore. We propose a novel 3D pre-training Vision-Language method, namely Multi-CLIP, that enables a model to learn language-grounded and transferable 3D scene point cloud representations.
  arXiv  Detail & Related papers  (2023-06-04T11:08:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.