论文标题
将旋转的物体视为高斯分布及其3-D概括
Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization
论文作者
论文摘要
现有检测方法通常使用参数化边界框（Bbox）进行建模和检测（水平）对象，并将其他旋转角度参数用于旋转对象。我们认为，这种机制在建立有效的旋转检测回归损失方面具有根本的局限性，尤其是对于高精度检测而言，高精度检测（例如0.75）。取而代之的是，我们建议将旋转的对象建模为高斯分布。一个直接的优势是，我们关于两个高斯之间距离的新回归损失，例如kullback-leibler Divergence（KLD）可以很好地对齐实际检测性能度量标准，这在现有方法中无法很好地解决。此外，两个瓶颈，即边界不连续性和类似方形的问题也消失了。我们还提出了一种高效的基于高斯度量的标签分配策略，以进一步提高性能。有趣的是，通过在基于高斯的KLD损失下分析Bbox参数的梯度，我们表明这些参数以可解释的物理意义进行了动态更新，这有助于解释我们方法的有效性，尤其是用于高精度检测。我们使用量身定制的算法设计将方法从2-D扩展到3-D，以处理标题估计，并在十二个公共数据集（2-D/3-D，空中/文本/脸部图像）上进行了实验结果，并带有各种基础检测器显示出其优越性。
Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects and an additional rotation angle parameter is used for rotated objects. We argue that such a mechanism has fundamental limitations in building an effective regression loss for rotation detection, especially for high-precision detection with high IoU (e.g. 0.75). Instead, we propose to model the rotated objects as Gaussian distributions. A direct advantage is that our new regression loss regarding the distance between two Gaussians e.g. Kullback-Leibler Divergence (KLD), can well align the actual detection performance metric, which is not well addressed in existing methods. Moreover, the two bottlenecks i.e. boundary discontinuity and square-like problem also disappear. We also propose an efficient Gaussian metric-based label assignment strategy to further boost the performance. Interestingly, by analyzing the BBox parameters' gradients under our Gaussian-based KLD loss, we show that these parameters are dynamically updated with interpretable physical meaning, which help explain the effectiveness of our approach, especially for high-precision detection. We extend our approach from 2-D to 3-D with a tailored algorithm design to handle the heading estimation, and experimental results on twelve public datasets (2-D/3-D, aerial/text/face images) with various base detectors show its superiority.
