标题： VoteSplat：霍夫投票高斯点云投影用于三维场景理解 显示英文标题

标题： VoteSplat: Hough Voting Gaussian Splatting for 3D Scene Understanding

摘要： 3D高斯点云（3DGS）已成为高质量、实时渲染的新视角合成3D场景的强力工具。 然而，现有方法主要关注几何和外观建模，缺乏更深层次的场景理解，同时带来了高昂的训练成本，这使得原本简化的可微分渲染流程变得复杂。 为此，我们提出了VoteSplat，一种新颖的3D场景理解框架，该框架将霍夫投票与3DGS相结合。 具体来说，使用分割一切模型（SAM）进行实例分割，提取物体并生成2D投票图。 然后我们将空间偏移向量嵌入到高斯基元中。 这些偏移通过将其与2D图像投票相关联来构建3D空间投票，而深度失真约束则在深度轴上细化定位。 对于开放词汇物体定位，VoteSplat通过投票点将2D图像语义映射到3D点云，降低了与高维CLIP特征相关的训练成本，同时保持了语义的明确性。 大量实验表明VoteSplat在开放词汇3D实例定位、3D点云理解、基于点击的3D物体定位、层次分割和消融研究中的有效性。 我们的代码可在https://sy-ja.github.io/votesplat/获取。 显示英文摘要

摘要： 3D Gaussian Splatting (3DGS) has become horsepower in high-quality, real-time rendering for novel view synthesis of 3D scenes. However, existing methods focus primarily on geometric and appearance modeling, lacking deeper scene understanding while also incurring high training costs that complicate the originally streamlined differentiable rendering pipeline. To this end, we propose VoteSplat, a novel 3D scene understanding framework that integrates Hough voting with 3DGS. Specifically, Segment Anything Model (SAM) is utilized for instance segmentation, extracting objects, and generating 2D vote maps. We then embed spatial offset vectors into Gaussian primitives. These offsets construct 3D spatial votes by associating them with 2D image votes, while depth distortion constraints refine localization along the depth axis. For open-vocabulary object localization, VoteSplat maps 2D image semantics to 3D point clouds via voting points, reducing training costs associated with high-dimensional CLIP features while preserving semantic unambiguity. Extensive experiments demonstrate effectiveness of VoteSplat in open-vocabulary 3D instance localization, 3D point cloud understanding, click-based 3D object localization, hierarchical segmentation, and ablation studies. Our code is available at https://sy-ja.github.io/votesplat/