Reason3D：通过大语言模型搜索与推理三维分割
Reason3D: Searching and Reasoning 3D Segmentation via Large Language Model
AI总结：
本文提出Reason3D，一个结合大语言模型与层次化掩码解码器的三维理解模型，通过由粗到细的分割策略，在ScanNet和Matterport3D上实现了三维推理分割、层次搜索与问答等任务。
摘要：
近年来，多模态大语言模型（LLMs）的进展在多个领域展现出巨大潜力，尤其是在概念推理方面。然而，它们在理解三维环境中的应用仍然有限，主要提供文本或数值输出，而无法生成稠密且信息丰富的分割掩码。本文介绍了Reason3D，一个专为全面三维理解而设计的新型LLM。Reason3D处理点云数据和文本提示，生成文本响应和分割掩码，从而支持三维推理分割、层次化搜索、表达性指代以及带详细掩码输出的问答等高级任务。我们提出了一种层次化掩码解码器，采用由粗到细的方法在广阔场景中分割物体。该解码器首先进行粗略位置估计，然后基于LLM根据文本查询预测的两个独特token进行物体掩码估计。在大规模ScanNet和Matterport3D数据集上的实验结果验证了Reason3D在多种任务上的有效性。
英文摘要：
Recent advancements in multimodal large language models (LLMs) have demonstrated significant potential across various domains, particularly in concept reasoning. However, their applications in understanding 3D environments remain limited, primarily offering textual or numerical outputs without generating dense, informative segmentation masks. This paper introduces Reason3D, a novel LLM designed for comprehensive 3D understanding. Reason3D processes point cloud data and text prompts to produce textual responses and segmentation masks, enabling advanced tasks such as 3D reasoning segmentation, hierarchical searching, express referring, and question answering with detailed mask outputs. We propose a hierarchical mask decoder that employs a coarse-to-fine approach to segment objects within expansive scenes. It begins with a coarse location estimation, followed by object mask estimation, using two unique tokens predicted by LLMs based on the textual query. Experimental results on large-scale ScanNet and Matterport3D datasets validate the effectiveness of our Reason3D across various tasks.
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。
