AI 中文总结
研究3D语义占用预测中高斯表示的分配问题，提出语义高斯分配变换器（SAGFormer），利用高斯属性和局部几何语义特征评分选高斯，实验表明该方法能改进占用预测，使高斯表示更优，显式容量分配是有用补充。
AI 中文摘要
语义3D高斯通过在体素监督下将语义基元渲染到体素体积中，为3D语义占用预测提供了一种紧凑的表示。最近的方法通过更灵活的基元形状、几何引导初始化和渐进致密化提高了这种表示的建模能力和效率。然而，这些进展主要决定了基元的表示、初始化或添加方式，没有明确解决当高斯总数必须限制以控制内存和计算时如何选择最有用的高斯。这种不平衡造成了分配瓶颈：简单区域存在冗余高斯，而困难区域得到的语义支持不足。我们提出了语义高斯分配变换器（SAGFormer），它使用高斯属性和局部几何语义特征对候选高斯进行评分并选择固定的最终高斯集。在nuScenes-SurroundOcc和SSCBench-KITTI-360上的实验表明，SAGFormer在评估协议下改进了占用预测，并产生了更语义一致和利用更好的高斯表示。在相似的最终数量和原始覆盖下，它减少了语义混合，加强了类一致的体素支持，并产生了更少未使用的高斯。结果表明，显式容量分配是对高斯细化进行语义占用预测的有用补充。
英文摘要
Semantic 3D Gaussians provide a compact representation for 3D semantic occupancy prediction by rendering semantic primitives into a voxel volume under voxel-wise supervision. Recent methods have improved the modeling ability and efficiency of this representation through more flexible primitive shapes, geometry-guided initialization, and progressive densification. However, these advances mainly determine how primitives are represented, initialized, or added, and do not explicitly address how to select the most useful Gaussians when their total number must be limited to control memory and computation. This imbalance creates an allocation bottleneck: redundant Gaussians remain in simple regions, while difficult regions receive insufficient semantic support. We propose the Semantic Gaussian Allocation Transformer (SAGFormer), which uses Gaussian attributes and local geometric-semantic features to score candidates and select a fixed final Gaussian set. Experiments on nuScenes-SurroundOcc and SSCBench-KITTI-360 show that SAGFormer improves occupancy prediction under the evaluated protocols and yields more semantically consistent and better-utilized Gaussian representations. Under similar final counts and raw coverage, it reduces semantic mixing, strengthens class-consistent voxel support, and produces fewer unused Gaussians. The results indicate that explicit capacity allocation is a useful complement to Gaussian refinement for semantic occupancy prediction.
