Optimized Minimal 3D Gaussian Splatting
Authors: name: Joo Chan Lee, name: Jong Hwan Ko, name: Eunbyung Park

3D Gaussian Splatting (3DGS) has emerged as a powerful representation for
real-time, high-performance rendering, enabling a wide range of applications.
However, representing 3D scenes with numerous explicit Gaussian primitives
imposes significant storage and memory overhead. Recent studies have shown that
high-quality rendering can be achieved with a substantially reduced number of
Gaussians when represented with high-precision attributes. Nevertheless,
existing 3DGS compression methods still rely on a relatively large number of
Gaussians, focusing primarily on attribute compression. This is because a
smaller set of Gaussians becomes increasingly sensitive to lossy attribute
compression, leading to severe quality degradation. Since the number of
Gaussians is directly tied to computational costs, it is essential to reduce
the number of Gaussians effectively rather than only optimizing storage. In
this paper, we propose Optimized Minimal Gaussians representation (OMG), which
significantly reduces storage while using a minimal number of primitives.
First, we determine the distinct Gaussian from the near ones, minimizing
redundancy without sacrificing quality. Second, we propose a compact and
precise attribute representation that efficiently captures both continuity and
irregularity among primitives. Additionally, we propose a sub-vector
quantization technique for improved irregularity representation, maintaining
fast training with a negligible codebook size. Extensive experiments
demonstrate that OMG reduces storage requirements by nearly 50% compared to the
previous state-of-the-art and enables 600+ FPS rendering while maintaining high
rendering quality. Our source code is available at
https://maincold2.github.io/omg/.