**Agent3D-Zero: An Agent for Zero-shot 3D Understanding** The paper introduces Agent3D-Zero, a novel framework that enables zero-shot 3D scene understanding using Vision-Language Models (VLMs). Unlike traditional methods that rely on fine-tuning Large Language Models (LLMs) with 3D data and texts, Agent3D-Zero leverages multiple images from diverse viewpoints to understand 3D scenes. The core idea is to reframe 3D scene perception as a process of synthesizing insights from multiple images, inspired by human cognitive abilities. Key components of Agent3D-Zero include: 1. **Set-of-Line Prompting (SoLP)**: This technique involves superimposing grid lines and tick marks on bird's-eye view (BEV) images to guide the VLM in selecting optimal camera viewpoints. 2. **Multi-viewpoint Synthesis**: The framework iteratively selects viewpoints to observe and summarize underlying knowledge, enhancing the VLM's ability to understand spatial relationships. 3. **Task-specific Prompts**: Agent3D-Zero can adapt to various tasks by employing specific prompts to guide the VLM in addressing questions or performing tasks such as question answering, caption generation, and dialogues. Experiments on datasets like ScanQA and Scannet v2 demonstrate the effectiveness of Agent3D-Zero in zero-shot 3D scene understanding, outperforming existing methods in tasks such as 3D question answering, 3D-assisted dialog, and 3D semantic segmentation. The framework's ability to handle complex 3D scenes without explicit 3D data structures showcases its potential for real-world applications in robotics, autonomous driving, and augmented reality.**Agent3D-Zero: An Agent for Zero-shot 3D Understanding** The paper introduces Agent3D-Zero, a novel framework that enables zero-shot 3D scene understanding using Vision-Language Models (VLMs). Unlike traditional methods that rely on fine-tuning Large Language Models (LLMs) with 3D data and texts, Agent3D-Zero leverages multiple images from diverse viewpoints to understand 3D scenes. The core idea is to reframe 3D scene perception as a process of synthesizing insights from multiple images, inspired by human cognitive abilities. Key components of Agent3D-Zero include: 1. **Set-of-Line Prompting (SoLP)**: This technique involves superimposing grid lines and tick marks on bird's-eye view (BEV) images to guide the VLM in selecting optimal camera viewpoints. 2. **Multi-viewpoint Synthesis**: The framework iteratively selects viewpoints to observe and summarize underlying knowledge, enhancing the VLM's ability to understand spatial relationships. 3. **Task-specific Prompts**: Agent3D-Zero can adapt to various tasks by employing specific prompts to guide the VLM in addressing questions or performing tasks such as question answering, caption generation, and dialogues. Experiments on datasets like ScanQA and Scannet v2 demonstrate the effectiveness of Agent3D-Zero in zero-shot 3D scene understanding, outperforming existing methods in tasks such as 3D question answering, 3D-assisted dialog, and 3D semantic segmentation. The framework's ability to handle complex 3D scenes without explicit 3D data structures showcases its potential for real-world applications in robotics, autonomous driving, and augmented reality.