Recent advancements in multimodal large language models (LLMs) have shown
their potential in various domains, especially concept reasoning. Despite these
developments, applications in understanding 3D environments remain limited.
This paper introduces Reason3D, a novel LLM designed for comprehensive 3D
understanding. Reason3D takes point cloud data and text prompts as input to
produce textual responses and segmentation masks, facilitating advanced tasks
like 3D reasoning segmentation, hierarchical searching, express referring, and
question answering with detailed mask outputs. Specifically, we propose a
hierarchical mask decoder to locate small objects within expansive scenes. This
decoder initially generates a coarse location estimate covering the object's
general area. This foundational estimation facilitates a detailed,
coarse-to-fine segmentation strategy that significantly enhances the precision
of object identification and segmentation. Experiments validate that Reason3D
achieves remarkable results on large-scale ScanNet and Matterport3D datasets
for 3D express referring, 3D question answering, and 3D reasoning segmentation
tasks. Code and models are available at:
this https URL

Reason3D 是一种新型的多模态大型语言模型，通过点云数据和文本提示作为输入，生成文本回答和分割遮罩，实现 3D 推理分割、分层搜索、精确引用和问题回答等高级任务。