We introduce a novel RGB-D patch descriptor designed for detecting coplanar
surfaces in SLAM reconstruction. The core of our method is a deep convolutional
neural net that takes in RGB, depth, and normal information of a planar patch
in an image and outputs a descriptor that can be used to find coplanar patches
from other images.We train the network on 10 million triplets of coplanar and
non-coplanar patches, and evaluate on a new coplanarity benchmark created from
commodity RGB-D scans. Experiments show that our learned descriptor outperforms
alternatives extended for this new task by a significant margin. In addition,
we demonstrate the benefits of coplanarity matching in a robust RGBD
reconstruction formulation.We find that coplanarity constraints detected with
our method are sufficient to get reconstruction results comparable to
state-of-the-art frameworks on most scenes, but outperform other methods on
standard benchmarks when combined with a simple keypoint method.

介绍了一种用于检测 SLAM 重建中共面表面的新型 RGB-D 补丁描述符，该方法的核心是一个深度卷积神经网络，它接受图像中平面贴片的 RGB、深度和法线信息，并输出可用于从其他图像中找到共平面贴片的描述符。我们在 1000 万个共面和非共面补丁的三元组上训练网络，并在从商品 RGB-D 扫描创建的新共面基准上进行评估。实验表明，我们学习的描述符在这项新任务中比已有的替代方案表现更好。此外，我们证明了共面匹配在稳健的 RGBD 重建公式中的优点。我们发现，使用我们的方法检测到的共面约束足以在大多数场景中获得可与现有的最先进框架相媲美的重建结果，但与简单的关键点方法相结合时，在标准基准测试上超过了其他方法。