Although great progress has been made in 3D visual grounding, current models still rely on explicit textual descriptions for grounding and lack the ability to reason human intentions from implicit instructions. We propose a new task called 3D reasoning grounding and introduce a new benchmark ScanReason which provides over 10K question-answer-location pairs from five reasoning types that require the synerization of reasoning and grounding. We further design our approach, ReGround3D, composed of the visual-centric reasoning module empowered by Multi-modal Large Language Model (MLLM) and the 3D grounding module to obtain accurate object locations by looking back to the enhanced geometry and fine-grained details from the 3D scenes. A chain-of-grounding mechanism is proposed to further boost the performance with interleaved reasoning and grounding steps during inference. Extensive experiments on the proposed benchmark validate the effectiveness of our proposed approach.

翻译：尽管三维视觉定位已取得显著进展，当前模型仍依赖显式文本描述进行定位，缺乏从隐含指令中推理人类意图的能力。我们提出了一项名为三维推理定位的新任务，并引入了一个新基准数据集ScanReason。该数据集提供了来自五种推理类型的超过一万个问答-位置对，这些类型均要求推理与定位能力的协同作用。我们进一步设计了方法ReGround3D，其由多模态大语言模型驱动的以视觉为中心的推理模块，以及三维定位模块构成。定位模块通过回溯三维场景中增强的几何结构与细粒度细节来获取精确的目标位置。我们提出了一种链式定位机制，通过在推理过程中交替进行推理与定位步骤，进一步提升性能。在提出的基准数据集上的大量实验验证了我们所提方法的有效性。