从单张图像估算交通标志尺寸，提升自动驾驶摄像头虚拟验证精度
Knowledge-Driven Dimension Estimation from a Single Image -3D Asset Generation Technology for Digital Twin Construction
- 分解物体为结构组件，结合设计规则与几何关系估算尺寸
- 通过组件间尺寸一致性约束，实现单图尺度估计误差小于15%
- 生成高保真3D资产，适用于数字孪生中的自动驾驶系统验证
在车载摄像头验证中，基于虚拟空间的仿真技术已能预评估各类场景下的误检与漏检。然而，虚拟环境与真实环境中物体尺度不一致会降低摄像头识别性能。对于安装在高处的交通标志，使用激光雷达或双目相机难以测距，需依赖单目图像进行尺寸估算。本文提出一种方法，将物体分解为多个结构单元，融合外部知识（设计规范、几何关系、常规尺寸）进行尺度估计。具体而言，该方法从单张图像中检测各组件，并结合其结构关联性与周围元素的尺寸一致性来推断各部分大小。进一步，基于估计结果重建三维资产。该方法可在数字孪生空间中放置接近真实尺度的3D资产，有望显著提升自动驾驶系统在虚拟环境中的摄像头验证准确性。
原文摘要 · Abstract (English)
In the verification of in-vehicle cameras, simulation technology using virtual spaces has advanced, enabling pre-evaluation of false detections and missed detections in various scenarios. However, discrepancies in the scale of the object being verified between the virtual and real environments can lead to a decrease in camera recognition performance. For traffic signs installed at high altitudes, distance measurement using LiDAR or stereo cameras is difficult, requiring size estimation from monocular images. This paper proposes a method for estimating the scale of an object by decomposing it into multiple structural elements and integrating external knowledge regarding design rules, geometric relationships, and conventional dimensions. Specifically, this method detects each component from a monocular image and estimates the size of each component by considering its structural relationships and dimensional consistency with surrounding elements. Furthermore, it generates a 3D asset of the object by reconstructing the estimated components. This method makes it possible to place 3D assets with a scale approximating the real environment within a digital twin space and is expected to contribute to improving the verification accuracy of in-vehicle cameras for autonomous driving in virtual environments.
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