Unseen 3D

Unseen 3D research focuses on developing methods for accurately perceiving, reconstructing, and interacting with three-dimensional objects that have not been encountered during training. Current efforts concentrate on leveraging techniques like neural radiance fields (NeRFs), graph neural networks, and diffusion models, often incorporating multi-modal data (RGB images, depth maps, force/torque sensors) to improve generalization capabilities. This work is significant for advancing robotics, particularly in manipulation and autonomous navigation, where robust handling of novel objects is crucial, and also has implications for computer vision and 3D modeling. The development of generalizable models that can handle unseen objects efficiently is a key challenge and area of active research.