Imagine an AI not just recognizing a chair, but understanding its individual parts – the wooden legs, the fabric seat, the metal frame. That's the groundbreaking capability of Kestrel, a new AI model that's changing how machines perceive the 3D world. Traditional AI struggles to grasp the intricate details of 3D objects, often seeing them as single, unified entities. Kestrel, however, delves deeper, identifying and locating individual parts like a human would. This part-level understanding is achieved through a novel approach called 'point grounding.' Kestrel doesn't just see a collection of points in a 3D scan; it connects those points to specific parts, like the handle of a teapot or the wheel of a car. This allows it to understand the object's structure and function in a far more nuanced way. To train Kestrel, researchers created a massive dataset, 3DCoMPaT-GRIN, filled with 3D models and detailed annotations of their parts. This dataset teaches Kestrel to link language with 3D shapes, enabling it to follow instructions like 'segment the metal handle' or 'show me the wooden legs.' The implications of this technology are vast. Imagine robots that can assemble furniture by understanding the relationship between parts, or AI assistants that can help you find the exact screw you need in a cluttered toolbox. Kestrel's ability to understand objects at a part level opens doors to a new era of human-machine interaction in the 3D world. While Kestrel represents a significant leap forward, challenges remain. Expanding the dataset to include more complex objects and part relationships is crucial for further progress. The future of AI and 3D understanding is bright, and Kestrel is leading the charge.