Agent3D-Zero is a novel 3D-aware agent framework that enables zero-shot 3D scene understanding without requiring explicit 3D data. The framework leverages Large Visual Language Models (VLMs) to actively select and interpret multiple viewpoints for 3D task resolution. By using custom-designed visual prompts, Agent3D-Zero processes a bird's-eye view image and iteratively selects subsequent viewpoints to summarize underlying knowledge. A key innovation is the introduction of Set-of-Line Prompting (SoLP), which enhances the VLM's ability to understand 3D spatial concepts by adding grid lines and directional markers to the BEV image. This approach allows the VLM to generate precise camera poses and interpret 3D scenes effectively. Agent3D-Zero demonstrates superior performance in various 3D reasoning and perception tasks, including 3D question answering, semantic segmentation, and dialogue tasks. It outperforms existing methods in the ScanQA dataset and other benchmarks, achieving high scores in metrics like METEOR, ROUGE-L, and CIDEr. The framework's zero-shot capability eliminates the need for annotated data, making it efficient and scalable. The system's ability to navigate complex 3D environments is further demonstrated through real-world navigation tasks, where it successfully identifies and reaches targets using only image-based observations. The framework's adaptability and effectiveness in multi-viewpoint synthesis and visual prompts highlight its potential for advancing 3D scene understanding and interaction. Overall, Agent3D-Zero represents a significant step forward in leveraging VLMs for 3D perception, offering a versatile and efficient solution for 3D scene analysis.Agent3D-Zero is a novel 3D-aware agent framework that enables zero-shot 3D scene understanding without requiring explicit 3D data. The framework leverages Large Visual Language Models (VLMs) to actively select and interpret multiple viewpoints for 3D task resolution. By using custom-designed visual prompts, Agent3D-Zero processes a bird's-eye view image and iteratively selects subsequent viewpoints to summarize underlying knowledge. A key innovation is the introduction of Set-of-Line Prompting (SoLP), which enhances the VLM's ability to understand 3D spatial concepts by adding grid lines and directional markers to the BEV image. This approach allows the VLM to generate precise camera poses and interpret 3D scenes effectively. Agent3D-Zero demonstrates superior performance in various 3D reasoning and perception tasks, including 3D question answering, semantic segmentation, and dialogue tasks. It outperforms existing methods in the ScanQA dataset and other benchmarks, achieving high scores in metrics like METEOR, ROUGE-L, and CIDEr. The framework's zero-shot capability eliminates the need for annotated data, making it efficient and scalable. The system's ability to navigate complex 3D environments is further demonstrated through real-world navigation tasks, where it successfully identifies and reaches targets using only image-based observations. The framework's adaptability and effectiveness in multi-viewpoint synthesis and visual prompts highlight its potential for advancing 3D scene understanding and interaction. Overall, Agent3D-Zero represents a significant step forward in leveraging VLMs for 3D perception, offering a versatile and efficient solution for 3D scene analysis.