Imagine teaching a computer to understand a 3D scene not through code, but through conversation. That’s the groundbreaking idea behind SegPoint, a new AI model that segments point clouds—collections of data points representing 3D objects—simply by understanding human language. Previously, 3D segmentation required explicit instructions, limiting AI’s flexibility in real-world applications like robotics and augmented reality. SegPoint changes this by leveraging the reasoning power of large language models (LLMs). Instead of relying on predefined categories or rigid commands, you can now tell SegPoint what you’re looking for in natural language. For example, instead of coding complex instructions to identify a chair, you could simply ask, “Where can I sit?” and SegPoint would highlight the corresponding points in the cloud. To make this possible, SegPoint incorporates a geometric enhancer that helps the model grasp the spatial relationships within the 3D scene. This module extracts geometric representations from the point cloud, allowing the LLM to better understand the user’s request in relation to the 3D structure. This is further enhanced by a ‘geometric-guided feature propagation’ module, ensuring the LLM extracts fine-grained details, which is essential for highly accurate segmentation masks. The researchers even created a new benchmark called Instruct3D, containing over 2,500 point cloud and instruction pairs, specifically designed to test an AI’s ability to comprehend implicit language commands. The results are impressive. SegPoint achieves state-of-the-art results on this new dataset, outperforming existing methods by a significant margin. Moreover, SegPoint achieves this performance across a range of 3D vision tasks—semantic, instance, and referring segmentation—using a single unified framework. SegPoint’s ability to understand implicit language offers enormous potential. Imagine a robot that can understand vague instructions, or an augmented reality application that can interact with the real world through natural language queries. While the current version only processes text prompts, future iterations will allow users to provide inputs via points and bounding boxes, bringing us closer to a future where interacting with 3D data is as intuitive as speaking to another human.