论文标题
3D场景几何学了解相机本地化的约束，并深入学习
3D Scene Geometry-Aware Constraint for Camera Localization with Deep Learning
论文作者
论文摘要
摄像机定位是自动驾驶车辆和移动机器人的基本和关键组成部分，可以在全球范围内定位，以进一步的环境感知，路径计划和运动控制。最近对基于卷积神经网络的端到端方法进行了大量研究，以实现甚至超过基于3D几何的传统方法。在这项工作中，我们提出了一个紧凑的网络，用于绝对相机姿势回归。受到传统方法的启发，还通过利用所有可用信息，包括运动，深度和图像内容来介绍3D场景几何感知约束。我们通过定义像素级的光度损失和图像级结构相似性损失，将此约束作为正规化项添加为正则化项。为了基准我们的方法，通过我们提出的方法和最先进的方法测试了包括室内和室外环境在内的各种具有挑战性的场景。实验结果表明，在预测准确性和收敛效率上，我们的方法的性能有了显着改善。
Camera localization is a fundamental and key component of autonomous driving vehicles and mobile robots to localize themselves globally for further environment perception, path planning and motion control. Recently end-to-end approaches based on convolutional neural network have been much studied to achieve or even exceed 3D-geometry based traditional methods. In this work, we propose a compact network for absolute camera pose regression. Inspired from those traditional methods, a 3D scene geometry-aware constraint is also introduced by exploiting all available information including motion, depth and image contents. We add this constraint as a regularization term to our proposed network by defining a pixel-level photometric loss and an image-level structural similarity loss. To benchmark our method, different challenging scenes including indoor and outdoor environment are tested with our proposed approach and state-of-the-arts. And the experimental results demonstrate significant performance improvement of our method on both prediction accuracy and convergence efficiency.
