The prevalence of relation networks in computer vision is in stark contrast
to underexplored point-based methods. In this paper, we explore the
possibilities of local relation operators and survey their feasibility. We
propose a scalable and efficient module, called group relation aggregator. The
module computes a feature of a group based on the aggregation of the features
of the inner-group points weighted by geometric relations and semantic
relations. We adopt this module to design our RPNet. We further verify the
expandability of RPNet, in terms of both depth and width, on the tasks of
classification and segmentation. Surprisingly, empirical results show that
wider RPNet fits for classification, while deeper RPNet works better on
segmentation. RPNet achieves state-of-the-art for classification and
segmentation on challenging benchmarks. We also compare our local aggregator
with PointNet++, with around 30% parameters and 50% computation saving.
Finally, we conduct experiments to reveal the robustness of RPNet with regard
to rigid transformation and noises.

本研究探讨了在计算机视觉中局部关系算子的可能性，提出了一种可扩展，有效的模块 ——group relation aggregator，并将其用于设计 RPNet。实验结果表明，对于分类而言，结构较宽的 RPNet 表现更佳，而对于分割而言，较深的 RPNet 更优。

学习点云内部关系

Learning Inner-Group Relations on Point Clouds

This paper addresses the task of relative camera pose estimation from raw
image pixels, by means of deep neural networks. The proposed RPNet network
takes pairs of images as input and directly infers the relative poses, without
the need of camera intrinsic/extrinsic. While state-of-the-art systems based on
SIFT + RANSAC, are able to recover the translation vector only up to scale,
RPNet is trained to produce the full translation vector, in an end-to-end way.
Experimental results on the Cambridge Landmark dataset show very promising
results regarding the recovery of the full translation vector. They also show
that RPNet produces more accurate and more stable results than traditional
approaches, especially for hard images (repetitive textures, textureless
images, etc). To the best of our knowledge, RPNet is the first attempt to
recover full translation vectors in relative pose estimation.

本文介绍了使用深度神经网络从原始图像像素中获取相对相机姿态估计的任务。提出的 RPNet 网络以成对的图像为输入，并直接推断相对姿态，无需相机内 / 外参数。通过使用 Cambridge 地标数据集上的实验结果表明，相对姿态的全翻译矢量的恢复很有前途，而且在传统方法不稳定的情况下更加准确和稳定。RPNet 是恢复相对姿态中全翻译矢量的首次尝试。