ScanReason：赋予3D视觉定位推理能力
ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities
AI总结：
针对3D视觉定位依赖显式文本的问题，提出3D推理定位任务及基准ScanReason，并设计ReGround3D方法，结合MLLM推理与3D定位模块及链式定位机制，有效提升性能。
摘要：
尽管3D视觉定位已经取得了巨大进展，但当前的模型仍然依赖于显式的文本描述进行定位，缺乏从隐含指令中推理人类意图的能力。我们提出了一个名为3D推理定位的新任务，并引入了一个新的基准ScanReason，该基准提供了超过10K个问答-位置对，涵盖五种推理类型，这些类型需要推理与定位的协同。我们进一步设计了我们的方法ReGround3D，该方法由多模态大语言模型（MLLM）增强的视觉中心推理模块和3D定位模块组成，通过回看3D场景中增强的几何和细粒度细节来获取准确的对象位置。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和定位步骤来进一步提升性能。在提出的基准上进行的大量实验验证了我们方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
