3D Gaussian as a New Vision Era: A Survey
Ben Fei Jingyi Xu Rui Zhang Qingyuan Zhou Weidong Yang Ying He
3D Gaussian Splatting (3D-GS) has emerged as a significant advancement in the
field of Computer Graphics, offering explicit scene representation and novel
view synthesis without the reliance on neural networks, such as Neural Radiance
Fields (NeRF). This technique has found diverse applications in areas such as
robotics, urban mapping, autonomous navigation, and virtual reality/augmented
reality, just name a few. Given the growing popularity and expanding research
in 3D Gaussian Splatting, this paper presents a comprehensive survey of
relevant papers from the past year. We organize the survey into taxonomies
based on characteristics and applications, providing an introduction to the
theoretical underpinnings of 3D Gaussian Splatting. Our goal through this
survey is to acquaint new researchers with 3D Gaussian Splatting, serve as a
valuable reference for seminal works in the field, and inspire future research
directions, as discussed in our concluding section.