3D Neural Field

3D neural fields represent 3D shapes and scenes as continuous functions learned by neural networks, offering advantages over discrete representations like meshes. Current research focuses on improving efficiency and scalability through novel architectures like triplanes and techniques such as local patch meshing, enabling faster and more memory-efficient processing of high-resolution data and facilitating applications like 3D reconstruction and generation from 2D images. These advancements are driving progress in areas such as high-quality 3D object reconstruction, interactive scene labeling, and human-aligned 3D shape perception, impacting fields ranging from computer graphics to robotics. The development of efficient diffusion models for 3D neural field generation is also a significant area of focus.