AdaGSFormer: Complexity-Adaptive Gaussian Evolution for 3D Semantic Occupancy Prediction

Gaussian-based representations offer an efficient alternative to dense volumetric features for 3D semantic occupancy prediction. Existing methods adapt the Gaussian population mainly through progressive densification or allocation under a predefined final budget, limiting their ability to increase capacity in structurally complex regions while removing redundancy elsewhere. We propose AdaGSFormer, a complexity-adaptive Gaussian evolution framework that adjusts the population size according to local scene structure without prescribing its final cardinality. AdaGSFormer predicts Gaussian-wise complexity scores using supervision derived from local semantic heterogeneity and uses scene-relative thresholds to guide population restructuring. It splits Gaussians in complex regions and removes redundant primitives through contribution-aware pruning and moment-preserving merging. To decode the resulting variable-size population, we formulate Gaussian contributions and Gaussian-to-voxel splatting in additive optical-density space. On the SurroundOcc dataset, AdaGSFormer achieves state-of-the-art performance in semantic occupancy prediction, while offering approximately $2.5\times$ faster inference and 57.8% lower GPU memory usage compared to representative Gaussian methods. These results demonstrate the effectiveness of complexity-adaptive Gaussian evolution for accurate and efficient 3D semantic occupancy prediction.