Understanding and Evaluating Hallucinations in 3D Visual Language Models

• URL: http://arxiv.org/abs/2502.15888v1
• Date: Tue, 18 Feb 2025 07:15:43 GMT
• Title: Understanding and Evaluating Hallucinations in 3D Visual Language Models
• Authors: Ruiying Peng, Kaiyuan Li, Weichen Zhang, Chen Gao, Xinlei Chen, Yong Li,
• Abstract summary: 3D-LLMs have been proposed to tackle complex tasks in embodied intelligence and scene understanding.<n>They are significantly affected by hallucinations.<n>This work presents the first systematic study of hallucinations in 3D-LLMs.
• Score: 42.355169504378246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, 3D-LLMs, which combine point-cloud encoders with large models, have been proposed to tackle complex tasks in embodied intelligence and scene understanding. In addition to showing promising results on 3D tasks, we found that they are significantly affected by hallucinations. For instance, they may generate objects that do not exist in the scene or produce incorrect relationships between objects. To investigate this issue, this work presents the first systematic study of hallucinations in 3D-LLMs. We begin by quickly evaluating hallucinations in several representative 3D-LLMs and reveal that they are all significantly affected by hallucinations. We then define hallucinations in 3D scenes and, through a detailed analysis of datasets, uncover the underlying causes of these hallucinations. We find three main causes: (1) Uneven frequency distribution of objects in the dataset. (2) Strong correlations between objects. (3) Limited diversity in object attributes. Additionally, we propose new evaluation metrics for hallucinations, including Random Point Cloud Pair and Opposite Question Evaluations, to assess whether the model generates responses based on visual information and aligns it with the text's meaning.

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