Agent3D-Zero: An Agent for Zero-shot 3D Understanding
Sha Zhang Di Huang Jiajun Deng Shixiang Tang Wanli Ouyang Tong He Yanyong Zhang
The ability to understand and reason the 3D real world is a crucial milestone
towards artificial general intelligence. The current common practice is to
finetune Large Language Models (LLMs) with 3D data and texts to enable 3D
understanding. Despite their effectiveness, these approaches are inherently
limited by the scale and diversity of the available 3D data. Alternatively, in
this work, we introduce Agent3D-Zero, an innovative 3D-aware agent framework
addressing the 3D scene understanding in a zero-shot manner. The essence of our
approach centers on reconceptualizing the challenge of 3D scene perception as a
process of understanding and synthesizing insights from multiple images,
inspired by how our human beings attempt to understand 3D scenes. By
consolidating this idea, we propose a novel way to make use of a Large Visual
Language Model (VLM) via actively selecting and analyzing a series of
viewpoints for 3D understanding. Specifically, given an input 3D scene,
Agent3D-Zero first processes a bird's-eye view image with custom-designed
visual prompts, then iteratively chooses the next viewpoints to observe and
summarize the underlying knowledge. A distinctive advantage of Agent3D-Zero is
the introduction of novel visual prompts, which significantly unleash the VLMs'
ability to identify the most informative viewpoints and thus facilitate
observing 3D scenes. Extensive experiments demonstrate the effectiveness of the
proposed framework in understanding diverse and previously unseen 3D
environments.