Open-vocabulary 3D scene understanding presents a significant challenge in the field. Recent advancements have sought to transfer knowledge embedded in vision language models from the 2D domain to 3D domain. However, these approaches often require learning prior knowledge from specific 3D scene datasets, which limits their applicability in open-world scenarios. The Segment Anything Model (SAM) has demonstrated remarkable zero-shot segmentation capabilities, prompting us to investigate its potential for comprehending 3D scenes without the need for training. In this paper, we introduce OV-SAM3D, a universal framework for open-vocabulary 3D scene understanding. This framework is designed to perform understanding tasks for any 3D scene without requiring prior knowledge of the scene. Specifically, our method is composed of two key sub-modules: First, we initiate the process by generating superpoints as the initial 3D prompts and refine these prompts using segment masks derived from SAM. Moreover, we then integrate a specially designed overlapping score table with open tags from the Recognize Anything Model (RAM) to produce final 3D instances with open-world label. Empirical evaluations conducted on the ScanNet200 and nuScenes datasets demonstrate that our approach surpasses existing open-vocabulary methods in unknown open-world environments.

OV-SAM3D是一个通用框架，用于不需要训练即可理解任何3D场景的开放词汇三维场景理解，通过使用Segment Anything Model (SAM)生成超点并通过Recognize Anything Model (RAM)的开放标签和操作表，结合超点和分割掩模生成最终的3D实例，经过对ScanNet200和nuScenes数据集的实证评估，我们的方法在未知的开放世界环境中超越了现有的开放词汇方法。

开放词汇SAM3D：理解任何三维场景