3D visual grounding aims to locate objects based on natural language descriptions in 3D scenes. Existing methods rely on a pre-defined Object Lookup Table (OLT) to query Visual Language Models (VLMs) for reasoning about object locations, which limits the applications in scenarios with undefined or unforeseen targets. To address this problem, we present OpenGround, a novel zero-shot framework for open-world 3D visual grounding. Central to OpenGround is the Active Cognition-based Reasoning (ACR) module, which is designed to overcome the fundamental limitation of pre-defined OLTs by progressively augmenting the cognitive scope of VLMs. The ACR module performs human-like perception of the target via a cognitive task chain and actively reasons about contextually relevant objects, thereby extending VLM cognition through a dynamically updated OLT. This allows OpenGround to function with both pre-defined and open-world categories. We also propose a new dataset named OpenTarget, which contains over 7000 object-description pairs to evaluate our method in open-world scenarios. Extensive experiments demonstrate that OpenGround achieves competitive performance on Nr3D, state-of-the-art on ScanRefer, and delivers a substantial 17.6% improvement on OpenTarget. Project Page at https://why-102.github.io/openground.io/.

翻译：三维视觉定位旨在根据自然语言描述在三维场景中定位物体。现有方法依赖预定义的对象查找表查询视觉语言模型以推理物体位置，这限制了其在目标未定义或不可预见场景中的应用。为解决该问题，我们提出OpenGround——一种面向开放世界三维视觉定位的新型零样本框架。其核心是主动认知推理模块，该模块通过渐进式扩展视觉语言模型的认知范围，旨在克服预定义对象查找表的根本局限性。该模块通过认知任务链对目标进行类人感知，并主动推理上下文相关对象，从而通过动态更新的对象查找表扩展视觉语言模型的认知能力。这使得OpenGround能够同时适用于预定义类别与开放世界类别。我们还提出了名为OpenTarget的新数据集，包含超过7000个物体-描述对，用于在开放世界场景中评估本方法。大量实验表明，OpenGround在Nr3D数据集上取得竞争性性能，在ScanRefer数据集上达到最优水平，并在OpenTarget数据集上实现17.6%的显著提升。项目页面详见https://why-102.github.io/openground.io/。