Aligning partial views of a scene into a single whole is essential to understanding one's environment and is a key component of numerous robotics tasks such as SLAM and SfM. Recent approaches have proposed end-to-end systems that can outperform traditional methods by leveraging pose supervision. However, with the rising prevalence of cameras with depth sensors, we can expect a new stream of raw RGB-D data without the annotations needed for supervision. We propose UnsupervisedR&R: an end-to-end unsupervised approach to learning point cloud registration from raw RGB-D video. The key idea is to leverage differentiable alignment and rendering to enforce photometric and geometric consistency between frames. We evaluate our approach on indoor scene datasets and find that we outperform existing traditional approaches with classic and learned descriptors while being competitive with supervised geometric point cloud registration approaches.

我们提出了一种无监督方法(UnsupervisedR&R)，从原始RGB-D视频中学习点云配准，通过可微的对齐和渲染，强制实现帧间的光度和几何一致性，实现三维场景的自动对齐。实验结果表明，该方法在室内场景数据集上优于传统方法，并具有竞争力的监督几何点云注册方法。

无监督点云配准：通过可微渲染实现的无监督点云配准