Zero Shot 3D

Zero-shot 3D segmentation aims to segment 3D objects or scenes into meaningful parts without requiring any training data specific to those objects. Current research heavily leverages pre-trained 2D models, like Segment Anything Model (SAM), adapting their capabilities to 3D data through techniques such as multi-view rendering, texture synthesis, and integration with large language models (LLMs). This approach shows promise for various applications, including medical image analysis, robotics, and autonomous driving, by enabling efficient and generalizable 3D understanding from limited labeled data. The focus is on improving accuracy and robustness across diverse 3D data types, including meshes and point clouds, and handling complex, fine-grained segmentation tasks.