The commonly adopted detect-then-match approach to registration finds difficulties in the cross-modality cases due to the incompatible keypoint detection and inconsistent feature description. We propose, 2D3D-MATR, a detection-free method for accurate and robust registration between images and point clouds. Our method adopts a coarse-to-fine pipeline where it first computes coarse correspondences between downsampled patches of the input image and the point cloud and then extends them to form dense correspondences between pixels and points within the patch region. The coarse-level patch matching is based on transformer which jointly learns global contextual constraints with self-attention and cross-modality correlations with cross-attention. To resolve the scale ambiguity in patch matching, we construct a multi-scale pyramid for each image patch and learn to find for each point patch the best matching image patch at a proper resolution level. Extensive experiments on two public benchmarks demonstrate that 2D3D-MATR outperforms the previous state-of-the-art P2-Net by around $20$ percentage points on inlier ratio and over $10$ points on registration recall. Our code and models are available at https://github.com/minhaolee/2D3DMATR.

翻译：常规的“先检测后匹配”配准方法在跨模态场景下由于关键点检测不兼容和特征描述不一致而面临困难。我们提出2D3D-MATR——一种用于图像与点云精确鲁棒配准的无检测方法。该方法采用从粗到细的流程：首先为输入图像与点云的下采样补丁计算粗粒度对应关系，进而将其扩展为补丁区域内像素点与三维点之间的密集对应。粗粒度补丁匹配基于Transformer架构，通过自注意力联合学习全局上下文约束，并通过交叉注意力学习跨模态相关性。为解决补丁匹配中的尺度歧义问题，我们为每个图像补丁构建多尺度金字塔，并学习为每个点补丁在合适分辨率层级上寻找最佳匹配图像补丁。在两个公开基准上的大量实验表明，2D3D-MATR在内点率上相比此前最优方法P2-Net提升约20个百分点，在配准召回率上提升超过10个百分点。我们的代码与模型已开源至 https://github.com/minhaolee/2D3DMATR。