通过建模高斯点间关系，实现快速精准的开放词汇3D分割
Relation-Centric Open-Vocabulary 3D Gaussian Segmentation
- 将3D高斯分割重构为点对点关系建模，避免逐点优化
- 在多个基准上达到当前最优，快50倍于传统方法
- 适合需要高效多粒度查询的3D场景理解任务
开放词汇3D高斯分割面临语言理解多样查询与精确边界分离的挑战。现有方法或在单个高斯点中嵌入语言信息以提升响应能力，或优化每个高斯点的实例特征以编码对象身份，但可能产生噪声分割或依赖高成本的逐场景优化。本文提出PairGS框架，将高斯分割重构为建模高斯点间成对关系。3D高斯表示提供丰富的关系估计信号，如视角贡献权重和多视角掩码证据。通过利用这些线索，PairGS在无需繁重优化的情况下显式构建关系图。首先使用低维描述子生成稀疏边候选，仅在候选上计算精确成对亲和度，并构建分层聚类树以支持多粒度查询。在开放词汇3D高斯分割基准上达到领先性能，其快速变体比基于优化的实例特征方法快50倍。
原文摘要 · Abstract (English)
Open-vocabulary 3D Gaussian segmentation is challenging because it requires language understanding for diverse queries and accurate separation of Gaussians along object boundaries. Prior approaches either embed language knowledge into individual Gaussians to improve query responsiveness or optimize per-Gaussian instance features to encode object identity. However, these strategies may produce noisy Gaussian segmentations or rely on cost-inefficient per-scene optimization. We propose PairGS, a framework that reframes Gaussian segmentation as modeling pairwise relations between Gaussians. 3D Gaussian representations provide rich signals for relation estimation, such as view contribution weights and multi-view mask evidence. By leveraging these cues, PairGS explicitly constructs a relation graph for segmentation without a heavy optimization process. PairGS first proposes sparse edge candidates using low-dimensional descriptors, computes precise pairwise affinities only on those candidates, and builds a hierarchical cluster tree for multi-granular querying. It achieves state-of-the-art results on open-vocabulary 3D Gaussian segmentation benchmarks, while the fast variant is 50x faster than optimization-based instance-feature approaches.
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