Successful point cloud registration relies on accurate correspondences established upon powerful descriptors. However, existing neural descriptors either leverage a rotation-variant backbone whose performance declines under large rotations, or encode local geometry that is less distinctive. To address this issue, we introduce RIGA to learn descriptors that are Rotation-Invariant by design and Globally-Aware. From the Point Pair Features (PPFs) of sparse local regions, rotation-invariant local geometry is encoded into geometric descriptors. Global awareness of 3D structures and geometric context is subsequently incorporated, both in a rotation-invariant fashion. More specifically, 3D structures of the whole frame are first represented by our global PPF signatures, from which structural descriptors are learned to help geometric descriptors sense the 3D world beyond local regions. Geometric context from the whole scene is then globally aggregated into descriptors. Finally, the description of sparse regions is interpolated to dense point descriptors, from which correspondences are extracted for registration. To validate our approach, we conduct extensive experiments on both object- and scene-level data. With large rotations, RIGA surpasses the state-of-the-art methods by a margin of 8\degree in terms of the Relative Rotation Error on ModelNet40 and improves the Feature Matching Recall by at least 5 percentage points on 3DLoMatch.
翻译:成功的云层注册取决于强大的描述符所建立的准确对应信息。然而,现有的神经描述符要么利用一个旋转变量主干,其性能在大规模旋转下下降,要么对不那么独特的本地几何进行编码。为了解决这个问题,我们引入了RIGA来学习通过设计和全球软件进行旋转的描述符。从稀少的当地区域的角角地貌(PPPFs)到全球范围将本地的旋转和不变化的几何测量方法编码成几何描述器。随后,全球对三维结构和几何背景的认识都以旋转不易的方式纳入。更具体地说,整个框架的3D结构首先由我们的全球PPF签名来代表,从中学习结构描述符来帮助地理描述3D世界在本地区域之外感知3D。然后,从整个场景点的几何背景被全球汇总成解码。最后,稀疏区域的描述被内插到密度点描述器,从中提取通信以供注册。为了验证我们的方法,我们用最差的模型,我们用高的比值数据进行广泛的实验。