对比高斯聚类：弱监督三维场景分割
Contrastive Gaussian Clustering: Weakly Supervised 3D Scene Segmentation
AI总结：
本文提出对比高斯聚类方法，通过为每个三维高斯学习分割特征向量，结合对比学习与空间正则化，在弱监督下实现跨视角一致的三维场景分割，并较现有技术提升IoU准确率8%。
摘要：
我们引入了对比高斯聚类（Contrastive Gaussian Clustering），这是一种新颖的方法，能够从任意视角提供分割掩码，并实现场景的三维分割。近期在新视角合成方面的研究展示了如何通过三维高斯点云来建模场景的外观，以及如何通过将高斯投影到平面上并在α混合其颜色之前生成给定视角的准确图像。借鉴这一范例，我们训练了一个模型，为每个高斯也包含一个分割特征向量。这些特征向量随后可用于三维场景分割，即根据其特征向量对高斯进行聚类；以及生成二维分割掩码，即将高斯投影到平面上并对分割特征进行α混合。通过结合对比学习和空间正则化，我们的方法可以在不一致的二维分割掩码上进行训练，并且仍然能够学习生成在所有视角下一致的分割掩码。此外，所得模型极为准确，将预测掩码的IoU准确率比现有技术水平提高了+8%。代码和训练好的模型将很快发布。
英文摘要：
We introduce Contrastive Gaussian Clustering, a novel approach capable of provide segmentation masks from any viewpoint and of enabling 3D segmentation of the scene. Recent works in novel-view synthesis have shown how to model the appearance of a scene via a cloud of 3D Gaussians, and how to generate accurate images from a given viewpoint by projecting on it the Gaussians before $α$ blending their color. Following this example, we train a model to include also a segmentation feature vector for each Gaussian. These can then be used for 3D scene segmentation, by clustering Gaussians according to their feature vectors; and to generate 2D segmentation masks, by projecting the Gaussians on a plane and $α$ blending over their segmentation features. Using a combination of contrastive learning and spatial regularization, our method can be trained on inconsistent 2D segmentation masks, and still learn to generate segmentation masks consistent across all views. Moreover, the resulting model is extremely accurate, improving the IoU accuracy of the predicted masks by $+8\%$ over the state of the art. Code and trained models will be released soon.
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。
