A review on 3D Gaussian splatting for sparse view reconstruction
Haitian Liu, Binglin Liu, Qianchao Hu, Peilun Du, Jing Li, Yang Bao, Feng Wang
Abstract
Sparse view 3D reconstruction remains challenging due to inherent data scale limitations. Mainstream sparse view 3D reconstruction algorithms based on the NeRF framework struggle to balance generation quality and real-time performance. Recently, the advent of 3D Gaussian Splatting technology has demonstrated remarkable results, becoming increasingly prominent in 3D scene representation and reconstruction. Exploring the application of 3D Gaussian Splatting technology for sparse view 3D reconstruction represents a promising research avenue. Based on this, our paper provides a comprehensive review of current sparse view 3D reconstruction methods leveraging 3D Gaussian Splatting, with an emphasis on extracting effective reconstruction information from input images and utilizing these data to generate realistic scenes efficiently and reliably. We then provide a detailed discussion on how the algorithm addresses issues such as artifacts and scale ambiguous, which are common challenges in this field. In the subsequent sections, we present both quantitative and qualitative comparisons of various sparse-view 3D reconstruction methods, roughly demonstrating the advantages of sparse view 3D Gaussian splatting methods in terms of reconstruction quality and efficiency. Furthermore, we analyze the potential applications of sparse view 3D Gaussian splatting methods. Finally, we identify the challenges faced by sparse-view 3D Gaussian splatting reconstruction and suggest potential solutions. We hope that our analysis will provide valuable insights for future research efforts.