We present a method for single image 3D cuboid object detection and
multi-view object SLAM in both static and dynamic environments, and demonstrate
that the two parts can improve each other. Firstly for single image object
detection, we generate high-quality cuboid proposals from 2D bounding boxes and
vanishing points sampling. The proposals are further scored and selected based
on the alignment with image edges. Secondly, multi-view bundle adjustment with
new object measurements is proposed to jointly optimize poses of cameras,
objects and points. Objects can provide long-range geometric and scale
constraints to improve camera pose estimation and reduce monocular drift.
Instead of treating dynamic regions as outliers, we utilize object
representation and motion model constraints to improve the camera pose
estimation. The 3D detection experiments on SUN RGBD and KITTI show better
accuracy and robustness over existing approaches. On the public TUM, KITTI
odometry and our own collected datasets, our SLAM method achieves the
state-of-the-art monocular camera pose estimation and at the same time,
improves the 3D object detection accuracy.

本文介绍了一种方法，用于在静态和动态环境下单个图像的三维立方体物体检测和多视图对象 SLAM，并展示了两个部分如何互相改进，对单个图像物体检测，我们从 2D 边界框和消失点采样中生成高质量的 cuboid 提议，并根据与图像边缘的对齐性对提议进行进一步评分和选择。其次，提出了新的对象测量的多视图束调整，以联合优化相机，对象和点的姿态。对象可以提供长程几何和尺度约束，以改善相机姿态估计并减少单眼漂移。而不是将动态区域视为异常值，我们利用对象表示和运动模型约束来改善相机姿态估计。在 SUN RGBD 和 KITTI 上进行的 3D 检测实验证明了与现有方法相比更高的准确性和鲁棒性。在公共 TUM，KITTI 测距和我们自己收集的数据集上，我们的 SLAM 方法实现了最先进的单眼相机姿态估计，同时提高了 3D 对象检测的准确性。