Pairwise frame registration with sparse geometric local features on real-world depth images is not particularly robust due to the low resolution and incomplete nature of the 3D scan data. Moreover, there might be many regions with similar geometric information. In this paper, we present 3DTDesc, a data-driven descriptor which closely combines both 2D texture and 3D geometric information for frame registration. The proposed descriptor is learned directly from color point clouds, which is time-efficient and provides robust and accurate geometric feature matching in a variety of settings. The texture information and the geometric information closely interact in the fusing network, which are complements of each other in situations of textureless regions or regions with similar geometric information and different texture information. We also propose a multi-scale 3DTDesc to further improve the performance of the feature matching. The effectiveness and efficiency of our proposed 3DTDesc are demonstrated by extensive experimental results on challenging RGB-D datasets and various ablation studies.

由于3D扫描数据的分辨率低和不完整，在现实世界的深度图像上具有稀疏几何局部特征的成对帧配准不是特别可靠。此外，可能有许多区域具有相似的几何信息。在本文中，我们介绍了3DTDesc，这是一种数据驱动的描述符，它紧密结合了2D纹理和3D几何信息以进行帧配准。所提出的描述符是直接从色点云中学习的，它是高效的，并且可以在各种设置中提供鲁棒且准确的几何特征匹配。纹理信息和几何信息在融合网络中紧密相互作用，在无纹理区域或具有相似几何信息和不同纹理信息的区域的情况下，它们相互补充。我们还提出了一种多尺度3DTDesc，以进一步提高特征匹配的性能。我们提出的3DTDesc的有效性和效率通过具有挑战性的RGB-D数据集的广泛实验结果和各种消融研究得到证明。