This paper presents a novel framework to learn a concise geometric primitive
representation for 3D point clouds. Different from representing each type of
primitive individually, we focus on the challenging problem of how to achieve a
concise and uniform representation robustly. We employ quadrics to represent
diverse primitives with only 10 parameters and propose the first end-to-end
learning-based framework, namely QuadricsNet, to parse quadrics in point
clouds. The relationships between quadrics mathematical formulation and
geometric attributes, including the type, scale and pose, are insightfully
integrated for effective supervision of QuaidricsNet. Besides, a novel
pattern-comprehensive dataset with quadrics segments and objects is collected
for training and evaluation. Experiments demonstrate the effectiveness of our
concise representation and the robustness of QuadricsNet. Our code is available
at https://github.com/MichaelWu99-lab/QuadricsNet

该论文提出了一种新颖的框架，用于学习 3D 点云的简洁几何基元表示。与分别表示每种基元不同，我们关注如何稳健地实现简洁且统一的表示问题。我们使用二次曲面来表示包含仅有 10 个参数的多种基元，并提出了首个端到端学习的框架 QuadricsNet 来解析点云中的二次曲面。二次曲面的数学表达与几何属性（包括类型、尺度和姿态）之间的关系被巧妙地集成，以对 QuaidricsNet 进行有效监督。此外，还收集了一个新颖的模式综合的数据集，其中包含二次曲面的分段和对象，用于训练和评估。实验证明了我们简洁表示的有效性和 QuadricsNet 的鲁棒性。我们的代码可在 https://github.com/MichaelWu99-lab/QuadricsNet 获得。

QuadricsNet: 学习点云中几何基元的简洁表示

QuadricsNet: Learning Concise Representation for Geometric Primitives in  Point Clouds

Simultaneous Localization And Mapping (SLAM) is a fundamental problem in
mobile robotics. While sparse point-based SLAM methods provide accurate camera
localization, the generated maps lack semantic information. On the other hand,
state of the art object detection methods provide rich information about
entities present in the scene from a single image. This work incorporates a
real-time deep-learned object detector to the monocular SLAM framework for
representing generic objects as quadrics that permit detections to be
seamlessly integrated while allowing the real-time performance. Finer
reconstruction of an object, learned by a CNN network, is also incorporated and
provides a shape prior for the quadric leading further refinement. To capture
the dominant structure of the scene, additional planar landmarks are detected
by a CNN-based plane detector and modeled as independent landmarks in the map.
Extensive experiments support our proposed inclusion of semantic objects and
planar structures directly in the bundle-adjustment of SLAM - Semantic SLAM -
that enriches the reconstructed map semantically, while significantly improving
the camera localization. The performance of our SLAM system is demonstrated in
this https URL and this https URL .

本文在单目 SLAM 框架中嵌入了实时深度学习的目标检测器，将普通物体表示为四面体，进一步细化物体重建，检测附加平面标志并建模为独立地标志，可大幅提高系统的定位性能和语义地图的丰富程度。