C3G：通过2K高斯学习紧凑的3D表示
C3G: Learning Compact 3D Representations with 2K Gaussians
AI总结：
本文提出C3G框架，通过学习可训练标记聚合多视图特征，生成紧凑且几何意义明确的3D高斯表示，提升场景重建与理解的效率和精度。
摘要：
从未对齐的稀疏视图中以前馈方式重建和理解3D场景仍是一个具有挑战性的问题。最近的方法使用像素级3D高斯点划法进行重建，随后进行2D到3D特征提升阶段以进行场景理解。然而，它们生成过多冗余的高斯，导致内存开销高和多视图特征聚合不优，从而降级了新视图合成和场景理解性能。我们提出C3G，一种新颖的前馈框架，仅在关键空间位置估计紧凑的3D高斯，从而减少冗余并实现有效的特征提升。我们引入可学习的标记通过自注意力聚合多视图特征以指导高斯生成，确保每个高斯整合来自不同视角的相关视觉特征。然后利用学习的注意力模式进行高斯解码以高效提升特征。在无姿态新视图合成、3D开放词汇分割和视图不变特征聚合的大量实验中展示了我们的方法的有效性。结果表明，紧凑且几何意义明确的表示足以实现高质量的场景重建和理解，相比现有方法实现了更高效的内存利用和特征保真度。
英文摘要：
Reconstructing and understanding 3D scenes from unposed sparse views in a feed-forward manner remains as a challenging task in 3D computer vision. Recent approaches use per-pixel 3D Gaussian Splatting for reconstruction, followed by a 2D-to-3D feature lifting stage for scene understanding. However, they generate excessive redundant Gaussians, causing high memory overhead and sub-optimal multi-view feature aggregation, leading to degraded novel view synthesis and scene understanding performance. We propose C3G, a novel feed-forward framework that estimates compact 3D Gaussians only at essential spatial locations, minimizing redundancy while enabling effective feature lifting. We introduce learnable tokens that aggregate multi-view features through self-attention to guide Gaussian generation, ensuring each Gaussian integrates relevant visual features across views. We then exploit the learned attention patterns for Gaussian decoding to efficiently lift features. Extensive experiments on pose-free novel view synthesis, 3D open-vocabulary segmentation, and view-invariant feature aggregation demonstrate our approach's effectiveness. Results show that a compact yet geometrically meaningful representation is sufficient for high-quality scene reconstruction and understanding, achieving superior memory efficiency and feature fidelity compared to existing methods.
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。
