标题： 3DRot：基于RGB的3D任务的3D旋转增强 显示英文标题

标题： 3DRot: 3D Rotation Augmentation for RGB-Based 3D Tasks

摘要： 基于RGB的3D任务，例如3D检测、深度估计、3D关键点估计，仍然受到稀缺且昂贵的标注和有限的增强工具的困扰，因为大多数图像变换，包括缩放和旋转，会破坏几何一致性。 在本文中，我们引入了3DRot，一种即插即用的增强方法，在围绕相机光学中心旋转和镜像图像的同时同步更新RGB图像、相机内参、物体姿态和3D标注，以保持投影几何——实现无需依赖任何场景深度的几何一致的旋转和反射。 我们通过一个经典的3D任务——单目3D检测来验证3DRot。 在SUN RGB-D数据集上，3DRot将$IoU_{3D}$从43.21提高到44.51，将旋转误差（ROT）从22.91$^\circ$降低到20.93$^\circ$，并将$mAP_{0.5}$从35.70提升到38.11。 作为比较，Cube R-CNN在单目3D估计中与其他3个数据集一起使用SUN RGB-D，具有相似的机制和测试数据集，将$IoU_{3D}$从36.2提高到37.8，并将$mAP_{0.5}$从34.7提高到35.4。 由于它纯粹通过相机空间变换操作，3DRot可以轻松转移到其他3D任务。 显示英文摘要

摘要： RGB-based 3D tasks, e.g., 3D detection, depth estimation, 3D keypoint estimation, still suffer from scarce, expensive annotations and a thin augmentation toolbox, since most image transforms, including resize and rotation, disrupt geometric consistency. In this paper, we introduce 3DRot, a plug-and-play augmentation that rotates and mirrors images about the camera's optical center while synchronously updating RGB images, camera intrinsics, object poses, and 3D annotations to preserve projective geometry-achieving geometry-consistent rotations and reflections without relying on any scene depth. We validate 3DRot with a classical 3D task, monocular 3D detection. On SUN RGB-D dataset, 3DRot raises $IoU_{3D}$ from 43.21 to 44.51, cuts rotation error (ROT) from 22.91$^\circ$ to 20.93$^\circ$, and boosts $mAP_{0.5}$ from 35.70 to 38.11. As a comparison, Cube R-CNN adds 3 other datasets together with SUN RGB-D for monocular 3D estimation, with a similar mechanism and test dataset, increases $IoU_{3D}$ from 36.2 to 37.8, boosts $mAP_{0.5}$ from 34.7 to 35.4. Because it operates purely through camera-space transforms, 3DRot is readily transferable to other 3D tasks.