Abstract & Pipeline

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.

overview

We initiate the process by overlaying grid lines and tick marks on the Bird's Eye View (BEV) images, constituting the prompt along with a scene type description. This prompt guides the Vision Language Model (VLM) to retrieve camera poses for images observing the 3D scene. The lower section demonstrates the versatility of Agent3D-Zero, showcasing its proficiency in addressing various 3D reasoning and perception tasks through strategic prompting and tool utilization.

Performance

Agent3D-Zero achieves state-of-the-art performance on ScanQA task. Here is the ranks in ScanQA test.

Citation

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