Imagine describing a part of a 3D object, like "the handle of the mug," and having an AI instantly highlight that exact part. Researchers are making this a reality with a groundbreaking approach to 3D segmentation called "Reasoning3D." This innovative technique allows users to interact with 3D models using natural language, bridging the gap between human descriptions and computer vision. Traditionally, 3D segmentation relied on predefined categories or extensive manual labeling. Reasoning3D, however, leverages the power of large language models (LLMs) to understand complex, nuanced queries. It works by rendering the 3D model from multiple viewpoints and then using a pre-trained 2D reasoning segmentation network to analyze each view. The AI then combines these 2D segmentations to create a complete 3D understanding of the object and its parts. This approach allows the system to handle implicit prompts, like "segment the part of the chair where you would sit," demonstrating a deeper understanding of object functionality and context. While still in its early stages, Reasoning3D shows promising results on various 3D models, including real-world scanned data. The researchers are even developing a user-friendly interface to make this technology more accessible. This breakthrough opens doors to exciting applications in fields like robotics, AR/VR, and even medical imaging. Imagine a surgeon asking an AI to highlight the precise area of a 3D organ model needing attention. The future of 3D interaction is here, and it's all about the power of words.