CAGS：基于上下文感知高斯溅射的开放词汇3D场景理解
CAGS: Open-Vocabulary 3D Scene Understanding with Context-Aware Gaussian Splatting
AI总结：
针对开放词汇3D场景理解中3DGS与开放词汇框架集成时的跨视角粒度不一致问题，提出CAGS框架，通过局部图上下文传播、掩码对比学习和预计算策略，提升3D实例分割性能并减少碎片化错误。
摘要：
开放词汇3D场景理解对于需要自然语言驱动空间解释的应用至关重要，例如机器人和增强现实。虽然3D高斯溅射（3DGS）为场景重建提供了强大的表示，但将其与开放词汇框架集成时揭示了一个关键挑战：跨视角粒度不一致。这一问题源于SAM等2D分割方法，导致跨视角的对象分割不一致（例如，“咖啡具”在一个视角中被分割为单个实体，但在另一个视角中被分割为“杯子+咖啡+勺子”）。现有的基于3DGS的方法通常依赖于孤立的每个高斯特征学习，忽略了连贯对象推理所需的空间上下文，导致碎片化表示。我们提出了上下文感知高斯溅射（CAGS），这是一种通过将空间上下文纳入3DGS来解决这一挑战的新框架。CAGS构建局部图以在高斯之间传播上下文特征，减少来自不一致粒度的噪声，采用以掩码为中心的对比学习来平滑跨视角的SAM衍生特征，并利用预计算策略通过预计算邻域关系来降低计算成本，从而实现大规模场景中的高效训练。通过集成空间上下文，CAGS在LERF-OVS和ScanNet等数据集上显著改进了3D实例分割并减少了碎片化错误，实现了稳健的语言引导3D场景理解。
英文摘要：
Open-vocabulary 3D scene understanding is crucial for applications requiring natural language-driven spatial interpretation, such as robotics and augmented reality. While 3D Gaussian Splatting (3DGS) offers a powerful representation for scene reconstruction, integrating it with open-vocabulary frameworks reveals a key challenge: cross-view granularity inconsistency. This issue, stemming from 2D segmentation methods like SAM, results in inconsistent object segmentations across views (e.g., a "coffee set" segmented as a single entity in one view but as "cup + coffee + spoon" in another). Existing 3DGS-based methods often rely on isolated per-Gaussian feature learning, neglecting the spatial context needed for cohesive object reasoning, leading to fragmented representations. We propose Context-Aware Gaussian Splatting (CAGS), a novel framework that addresses this challenge by incorporating spatial context into 3DGS. CAGS constructs local graphs to propagate contextual features across Gaussians, reducing noise from inconsistent granularity, employs mask-centric contrastive learning to smooth SAM-derived features across views, and leverages a precomputation strategy to reduce computational cost by precomputing neighborhood relationships, enabling efficient training in large-scale scenes. By integrating spatial context, CAGS significantly improves 3D instance segmentation and reduces fragmentation errors on datasets like LERF-OVS and ScanNet, enabling robust language-guided 3D scene understanding.
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。
