SplatSSC：解耦深度引导的高斯点云法用于语义场景补全
SplatSSC: Decoupled Depth-Guided Gaussian Splatting for Semantic Scene Completion
AI总结：
SplatSSC通过深度引导的初始化策略和解耦高斯聚合器，提升语义场景补全的效率与鲁棒性，实现性能和资源消耗的优化。
摘要：
单目三维语义场景补全（SSC）是一项具有挑战性但前景广阔的任务，旨在从单张图像中推断出场景的密集几何和语义描述。尽管近期基于物体的范式通过利用灵活的3D高斯素形单元显著提高了效率，但它们仍然严重依赖大量随机初始化的素形单元，这不可避免地导致了1）低效的素形单元初始化和2）引入错误人工的异常素形单元。在本文中，我们提出了SplatSSC，一个新颖的框架，通过深度引导的初始化策略和原则性的高斯聚合器来解决这些限制。与随机初始化不同，SplatSSC利用一个专门的深度分支，该分支由组级多尺度融合（GMF）模块组成，整合多尺度图像和深度特征，以生成稀疏但具有代表性的初始高斯素形单元。为了减轻异常素形单元带来的噪声，我们开发了解耦高斯聚合器（DGA），通过在高斯到体素点云法过程中分解几何和语义预测来增强鲁棒性。配合专门的概率尺度损失，我们的方法在Occ-ScanNet数据集上实现了最先进的性能，比先前方法在IoU上提高了6.3%，在mIoU上提高了4.1%，同时将延迟和内存成本分别降低了超过9.3%。
英文摘要：
Monocular 3D Semantic Scene Completion (SSC) is a challenging yet promising task that aims to infer dense geometric and semantic descriptions of a scene from a single image. While recent object-centric paradigms significantly improve efficiency by leveraging flexible 3D Gaussian primitives, they still rely heavily on a large number of randomly initialized primitives, which inevitably leads to 1) inefficient primitive initialization and 2) outlier primitives that introduce erroneous artifacts. In this paper, we propose SplatSSC, a novel framework that resolves these limitations with a depth-guided initialization strategy and a principled Gaussian aggregator. Instead of random initialization, SplatSSC utilizes a dedicated depth branch composed of a Group-wise Multi-scale Fusion (GMF) module, which integrates multi-scale image and depth features to generate a sparse yet representative set of initial Gaussian primitives. To mitigate noise from outlier primitives, we develop the Decoupled Gaussian Aggregator (DGA), which enhances robustness by decomposing geometric and semantic predictions during the Gaussian-to-voxel splatting process. Complemented with a specialized Probability Scale Loss, our method achieves state-of-the-art performance on the Occ-ScanNet dataset, outperforming prior approaches by over 6.3% in IoU and 4.1% in mIoU, while reducing both latency and memory cost by more than 9.3%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
