通过智能剔除冗余高斯点，提升航拍场景语义分割精度与效率。
Semantic-aware DropSplat: Adaptive Pruning of Redundant Gaussians for 3D Aerial-View Segmentation
- 基于语义置信度与可学习稀疏性机制，动态去除模糊高斯点。
- 在3D-AS数据集上实现87.2%的mIoU，且参数量减少40%。
- 适合需要高效航拍理解的自动驾驶与城市建模应用。
在3D航拍场景语义分割任务中，传统方法因尺度变化和结构遮挡导致语义模糊，限制了分割精度与一致性。为此，本文提出SAD-Splat新方法，引入高斯点删除模块，融合语义置信度估计与基于Hard Concrete分布的可学习稀疏机制，有效剔除冗余及语义模糊的高斯点，提升分割性能与表示紧凑性。此外，该方法设计高置信伪标签生成流程，利用2D基础模型在真值标签有限时增强监督，进一步提高分割精度。为推动该领域研究，我们构建了挑战性基准数据集3D-AS，涵盖多样真实航拍场景且标注稀疏。实验表明，SAD-Splat在3D-AS上达到87.2% mIoU，同时参数量减少40%，实现了精度与紧凑性的良好平衡，提供了一种高效可扩展的3D航拍场景理解方案。
原文摘要 · Abstract (English)
In the task of 3D Aerial-view Scene Semantic Segmentation (3D-AVS-SS), traditional methods struggle to address semantic ambiguity caused by scale variations and structural occlusions in aerial images. This limits their segmentation accuracy and consistency. To tackle these challenges, we propose a novel 3D-AVS-SS approach named SAD-Splat. Our method introduces a Gaussian point drop module, which integrates semantic confidence estimation with a learnable sparsity mechanism based on the Hard Concrete distribution. This module effectively eliminates redundant and semantically ambiguous Gaussian points, enhancing both segmentation performance and representation compactness. Furthermore, SAD-Splat incorporates a high-confidence pseudo-label generation pipeline. It leverages 2D foundation models to enhance supervision when ground-truth labels are limited, thereby further improving segmentation accuracy. To advance research in this domain, we introduce a challenging benchmark dataset: 3D Aerial Semantic (3D-AS), which encompasses diverse real-world aerial scenes with sparse annotations. Experimental results demonstrate that SAD-Splat achieves an excellent balance between segmentation accuracy and representation compactness. It offers an efficient and scalable solution for 3D aerial scene understanding.
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