Real World Object

Research on real-world object representation focuses on accurately capturing and digitally reconstructing the geometry, physical properties, and interactions of objects for applications in robotics and computer vision. Current efforts utilize various model architectures, including neural radiance fields (NeRFs), Gaussian splatting, and large language models (LLMs), often incorporating multi-view images, point clouds, and even tactile data to achieve high-fidelity 3D models and enable tasks like object manipulation and scene understanding. This work is significant because accurate digital twins of real-world objects are crucial for advancing robotics, virtual and augmented reality, and other fields requiring seamless interaction between the physical and digital worlds. The development of robust and efficient methods for object representation is driving progress in these areas.