HoGS: Unified Near and Far Object Reconstruction via Homogeneous Gaussian Splatting
This work addresses a bottleneck in novel view synthesis for outdoor scene reconstruction, offering an incremental improvement over existing 3D Gaussian splatting methods.
The paper tackled the problem of 3D Gaussian splatting's poor performance on distant objects in unbounded outdoor environments by proposing HoGS, which uses homogeneous coordinates to unify near and far object reconstruction, resulting in significantly enhanced accuracy for distant objects while maintaining high-quality rendering and fast training speeds.
Novel view synthesis has demonstrated impressive progress recently, with 3D Gaussian splatting (3DGS) offering efficient training time and photorealistic real-time rendering. However, reliance on Cartesian coordinates limits 3DGS's performance on distant objects, which is important for reconstructing unbounded outdoor environments. We found that, despite its ultimate simplicity, using homogeneous coordinates, a concept on the projective geometry, for the 3DGS pipeline remarkably improves the rendering accuracies of distant objects. We therefore propose Homogeneous Gaussian Splatting (HoGS) incorporating homogeneous coordinates into the 3DGS framework, providing a unified representation for enhancing near and distant objects. HoGS effectively manages both expansive spatial positions and scales particularly in outdoor unbounded environments by adopting projective geometry principles. Experiments show that HoGS significantly enhances accuracy in reconstructing distant objects while maintaining high-quality rendering of nearby objects, along with fast training speed and real-time rendering capability. Our implementations are available on our project page https://kh129.github.io/hogs/.