In this paper, we introduce PCR-CG: a novel 3D point cloud registration module explicitly embedding the color signals into the geometry representation. Different from previous methods that only use geometry representation, our module is specifically designed to effectively correlate color into geometry for the point cloud registration task. Our key contribution is a 2D-3D cross-modality learning algorithm that embeds the deep features learned from color signals to the geometry representation. With our designed 2D-3D projection module, the pixel features in a square region centered at correspondences perceived from images are effectively correlated with point clouds. In this way, the overlapped regions can be inferred not only from point cloud but also from the texture appearances. Adding color is non-trivial. We compare against a variety of baselines designed for adding color to 3D, such as exhaustively adding per-pixel features or RGB values in an implicit manner. We leverage Predator [25] as the baseline method and incorporate our proposed module onto it. To validate the effectiveness of 2D features, we ablate different 2D pre-trained networks and show a positive correlation between the pre-trained weights and the task performance. Our experimental results indicate a significant improvement of 6.5% registration recall over the baseline method on the 3DLoMatch benchmark. We additionally evaluate our approach on SOTA methods and observe consistent improvements, such as an improvement of 2.4% registration recall over GeoTransformer as well as 3.5% over CoFiNet. Our study reveals a significant advantages of correlating explicit deep color features to the point cloud in the registration task.
翻译:在本文中, 我们引入了 PCR- CG : 一个新型的 3D 点云登记模块, 将颜色信号明确嵌入几何代表制中。 不同于以往仅使用几何表示法的方法, 我们的模块是专门设计, 将颜色与点云登记任务的几何测量法有效地联系起来。 我们的主要贡献是 2D-3D 交叉模式学习算法, 将从颜色信号中学到的深点特征嵌入几何代表制。 我们设计的 2D-3D 投影模块, 以图像所见的通信为中心的一个平方区域中的像素特征与点云有效关联。 这样, 重叠的区域不仅可以从点云中, 也可以从质表表上推断出。 添加颜色是非三维的。 我们的主要贡献是, 与为向 3D 添加彩色信号而设计的各种基线, 比如, 我们利用 Pedator [ 25] 作为基线方法, 并纳入我们提议的模块。 为了验证 2D 特征的有效性, 我们用不同的 2D 预先的颜色 网络, 以及纹显示 质 的显示我们相当的升级的网络的升级的网络, 的升级的升级的进度, 显示我们之前的升级的进度的进度的进度的进度的进度, 的进度的进度的进度的进度, 表明我们 的进度的进度的进度的进度的进度的进度的进度的进度的进度的进度, 。</s>