Abstract
3D Gaussian Splatting (3D-GS) has emerged as an efficient 3D representation and a promising foundation for semantic tasks like segmentation. However, existing 3D-GS-based segmentation methods typically rely on high-dimensional category features, which introduce substantial memory overhead. Moreover, fine-grained segmentation remains challenging due to label space congestion and the lack of stable multi-granularity control mechanisms. To address these limitations, we propose a coarse-to-fine binary encoding scheme for per-Gaussian category representation, which compresses each feature into a single integer via the binary-to-decimal mapping, drastically reducing memory usage. We further design a progressive training strategy that decomposes panoptic segmentation into a series of independent sub-tasks, reducing inter-class conflicts and thereby enhancing fine-grained segmentation capability. Additionally, we fine-tune opacity during segmentation training to address the incompatibility between photometric rendering and semantic segmentation, which often leads to foreground-background confusion. Extensive experiments on multiple benchmarks demonstrate that our method achieves state-of-the-art segmentation performance while significantly reducing memory consumption and accelerating inference.
Abstract (translated)
3D高斯点绘（3D-GS）作为一种高效的三维表示方法，已成为语义任务如分割的基础，并展现出巨大潜力。然而，现有的基于3D-GS的分割方法通常依赖于高维类别特征，这会带来显著的内存开销。此外，由于标签空间拥挤和缺乏稳定的多粒度控制机制，细粒度分割仍然具有挑战性。为了解决这些限制，我们提出了一种从粗到细的二进制编码方案，用于每个高斯点类别的表示。该方案将每个特征压缩成一个单一整数，通过二进制到十进制的映射，大大减少了内存使用量。此外，我们还设计了一个渐进式训练策略，将全景分割分解为一系列独立子任务，从而减少类别间的冲突，并提高细粒度分割能力。另外，在分割训练过程中微调不透明度，以解决光度渲染与语义分割之间的兼容性问题，这通常会导致前景和背景的混淆。 在多个基准测试上的广泛实验表明，我们的方法不仅实现了最先进的分割性能，还显著减少了内存消耗并加速了推理过程。
URL
https://arxiv.org/abs/2512.00944
