D3Former: Jointly Learning Repeatable Dense Detectors and
  Feature-enhanced Descriptors via Saliency-guided Transformer
Junjie Gao Pengfei Wang Qiujie Dong Qiong Zeng Shiqing Xin Caiming Zhang
Establishing accurate and representative matches is a crucial step in
addressing the point cloud registration problem. A commonly employed approach
involves detecting keypoints with salient geometric features and subsequently
mapping these keypoints from one frame of the point cloud to another. However,
methods within this category are hampered by the repeatability of the sampled
keypoints. In this paper, we introduce a saliency-guided trans\textbf{former},
referred to as \textit{D3Former}, which entails the joint learning of
repeatable \textbf{D}ense \textbf{D}etectors and feature-enhanced
\textbf{D}escriptors. The model comprises a Feature Enhancement Descriptor
Learning (FEDL) module and a Repetitive Keypoints Detector Learning (RKDL)
module. The FEDL module utilizes a region attention mechanism to enhance
feature distinctiveness, while the RKDL module focuses on detecting repeatable
keypoints to enhance matching capabilities. Extensive experimental results on
challenging indoor and outdoor benchmarks demonstrate that our proposed method
consistently outperforms state-of-the-art point cloud matching methods.
Notably, tests on 3DLoMatch, even with a low overlap ratio, show that our
method consistently outperforms recently published approaches such as RoReg and
RoITr. For instance, with the number of extracted keypoints reduced to 250, the
registration recall scores for RoReg, RoITr, and our method are 64.3\%, 73.6\%,
and 76.5\%, respectively.