仅用单目图像和少量点击，实现高精度车辆3D立方体标注。
ToosiCubix: Monocular 3D Cuboid Labeling via Vehicle Part Annotations
- 通过标注车轮、标识等部件，结合几何约束优化估计车辆姿态与尺寸。
- 每车仅需约10次点击，支持9自由度标注，精度接近真实数据。
- 适合无激光雷达或双目设备的团队快速添加3D标注。
现有车辆3D立方体标注方法多依赖昂贵且需精确校准的相机-LiDAR或立体视觉系统，限制了大规模数据采集的可行性。本文提出ToosiCubix，一种仅需单目图像与相机内参即可完成高质量3D立方体标注的简单高效方法。该方法仅需每辆车约10次用户点击，通过标注车轮、车标等部件特征，结合几何约束构建优化问题，采用坐标下降策略交替求解透视n点（PnP）与最小二乘子问题，实现8自由度的位置、朝向与尺寸估计（存在尺度模糊）。为解决尺度及未观测维度等常见模糊性，引入概率尺寸先验，实现9自由度立方体定位。在KITTI与Cityscapes3D数据集上验证表明，本方法能以低成本、可扩展的方式生成高质量3D标注。
原文摘要 · Abstract (English)
Many existing methods for 3D cuboid annotation of vehicles rely on expensive and carefully calibrated camera-LiDAR or stereo setups, limiting their accessibility for large-scale data collection. We introduce ToosiCubix, a simple yet powerful approach for annotating ground-truth cuboids using only monocular images and intrinsic camera parameters. Our method requires only about 10 user clicks per vehicle, making it highly practical for adding 3D annotations to existing datasets originally collected without specialized equipment. By annotating specific features (e.g., wheels, car badge, symmetries) across different vehicle parts, we accurately estimate each vehicle's position, orientation, and dimensions up to a scale ambiguity (8 DoF). The geometric constraints are formulated as an optimization problem, which we solve using a coordinate descent strategy, alternating between Perspective-n-Points (PnP) and least-squares subproblems. To handle common ambiguities such as scale and unobserved dimensions, we incorporate probabilistic size priors, enabling 9 DoF cuboid placements. We validate our annotations against the KITTI and Cityscapes3D datasets, demonstrating that our method offers a cost-effective and scalable solution for high-quality 3D cuboid annotation.
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