3D Question Answering via only 2D Vision-Language Models

• URL: http://arxiv.org/abs/2505.22143v1
• Date: Wed, 28 May 2025 09:04:39 GMT
• Title: 3D Question Answering via only 2D Vision-Language Models
• Authors: Fengyun Wang, Sicheng Yu, Jiawei Wu, Jinhui Tang, Hanwang Zhang, Qianru Sun,
• Abstract summary: Large vision-language models (LVLMs) have advanced numerous fields.<n>We explore how to harness their potential to address 3D scene understanding tasks, using 3D question answering (3D-QA) as a representative example.<n>Specifically, we sample 2D views from a 3D point cloud and feed them into 2D models to answer a given question.<n>We propose cdViews, a novel approach to automatically selecting critical and diverse Views for 3D-QA.
• Score: 87.41421075243103
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large vision-language models (LVLMs) have significantly advanced numerous fields. In this work, we explore how to harness their potential to address 3D scene understanding tasks, using 3D question answering (3D-QA) as a representative example. Due to the limited training data in 3D, we do not train LVLMs but infer in a zero-shot manner. Specifically, we sample 2D views from a 3D point cloud and feed them into 2D models to answer a given question. When the 2D model is chosen, e.g., LLAVA-OV, the quality of sampled views matters the most. We propose cdViews, a novel approach to automatically selecting critical and diverse Views for 3D-QA. cdViews consists of two key components: viewSelector prioritizing critical views based on their potential to provide answer-specific information, and viewNMS enhancing diversity by removing redundant views based on spatial overlap. We evaluate cdViews on the widely-used ScanQA and SQA benchmarks, demonstrating that it achieves state-of-the-art performance in 3D-QA while relying solely on 2D models without fine-tuning. These findings support our belief that 2D LVLMs are currently the most effective alternative (of the resource-intensive 3D LVLMs) for addressing 3D tasks.

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