Implicit Neural 3D Representation

Implicit neural 3D representations aim to encode 3D shapes and scenes as continuous functions, enabling novel view synthesis and efficient shape manipulation. Current research focuses on improving generalization across diverse scenes, enhancing compactness and reducing reliance on massive datasets, and developing structured representations for deformable objects, often leveraging architectures like neural radiance fields (NeRFs) and variations incorporating techniques such as double height fields and codebook priors. These advancements are significant for applications ranging from computer graphics and virtual reality to robotics and medical imaging, offering more efficient and robust methods for 3D data representation and manipulation.