OpenMaskDINO3D : Reasoning 3D Segmentation via Large Language Model
This addresses the problem of enabling more intuitive human-AI interaction in 3D environments for applications like robotics or augmented reality, though it appears incremental as it extends 2D reasoning segmentation concepts to 3D.
The paper tackles the lack of 3D reasoning segmentation systems that can interpret implicit natural language instructions, introducing OpenMaskDINO3D, which processes point clouds and text prompts to produce instance segmentation masks, achieving high-precision results validated on ScanNet datasets.
Although perception systems have made remarkable advancements in recent years, particularly in 2D reasoning segmentation, these systems still rely on explicit human instruction or pre-defined categories to identify target objects before executing visual recognition tasks. Such systems have matured significantly, demonstrating the ability to reason and comprehend implicit user intentions in two-dimensional contexts, producing accurate segmentation masks based on complex and implicit query text. However, a comparable framework and structure for 3D reasoning segmentation remain absent. This paper introduces OpenMaskDINO3D, a LLM designed for comprehensive 3D understanding and segmentation. OpenMaskDINO3D processes point cloud data and text prompts to produce instance segmentation masks, excelling in many 3D tasks. By introducing a SEG token and object identifier, we achieve high-precision 3D segmentation mask generation, enabling the model to directly produce accurate point cloud segmentation results from natural language instructions. Experimental results on large-scale ScanNet datasets validate the effectiveness of our OpenMaskDINO3D across various tasks.