GS4: 可泛化的稀疏点溅 semantic SLAM
GS4: Generalizable Sparse Splatting Semantic SLAM
AI总结：
GS4是一种可泛化的基于高斯点溅的语义SLAM系统，通过高效优化和减少高斯点数量，实现更快速和更精确的3D地图生成与跟踪。
摘要：
传统SLAM算法在相机跟踪方面表现出色，但通常会产生不完整且低分辨率的地图，这些地图与语义预测不紧密集成。近期的研究将高斯点溅（GS）整合到SLAM中，以实现密集且逼真的3D地图，但现有的基于GS的SLAM方法需要每场景优化，速度慢且消耗过多的高斯点。我们提出了GS4，这是首个可泛化的基于GS的语义SLAM系统。与先前方法相比，GS4运行速度快10倍，使用高斯点少10倍，并在颜色、深度、语义地图和相机跟踪方面实现了最先进的性能。从RGB-D视频流中，GS4使用前馈网络逐步构建和更新一组3D高斯点。首先，高斯预测模型从输入帧估计稀疏的高斯参数集，这将颜色和语义预测与相同的主干网络结合起来。然后，高斯细化网络将新高斯点与现有集合合并，同时避免冗余。最后，当检测到显著姿态变化时，我们仅进行1-5次联合高斯-姿态优化，以纠正漂移、去除漂浮点并进一步提高跟踪精度。在现实世界中的ScanNet和ScanNet++基准测试中，GS4展示了最先进的语义SLAM性能，通过零样本迁移至NYUv2和TUM RGB-D数据集，证明了其强大的泛化能力。
英文摘要：
Traditional SLAM algorithms excel at camera tracking, but typically produce incomplete and low-resolution maps that are not tightly integrated with semantics prediction. Recent work integrates Gaussian Splatting (GS) into SLAM to enable dense, photorealistic 3D mapping, yet existing GS-based SLAM methods require per-scene optimization that is slow and consumes an excessive number of Gaussians. We present GS4, the first generalizable GS-based semantic SLAM system. Compared with prior approaches, GS4 runs 10x faster, uses 10x fewer Gaussians, and achieves state-of-the-art performance across color, depth, semantic mapping and camera tracking. From an RGB-D video stream, GS4 incrementally builds and updates a set of 3D Gaussians using a feed-forward network. First, the Gaussian Prediction Model estimates a sparse set of Gaussian parameters from input frame, which integrates both color and semantic prediction with the same backbone. Then, the Gaussian Refinement Network merges new Gaussians with the existing set while avoiding redundancy. Finally, when significant pose changes are detected, we perform only 1-5 iterations of joint Gaussian-pose optimization to correct drift, remove floaters, and further improve tracking accuracy. Experiments on the real-world ScanNet and ScanNet++ benchmarks demonstrate state-of-the-art semantic SLAM performance, with strong generalization capability shown through zero-shot transfer to the NYUv2 and TUM RGB-D datasets.
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