论文标题
通过空间整流器对倾斜图像的表面正常估计
Surface Normal Estimation of Tilted Images via Spatial Rectifier
论文作者
论文摘要
在本文中，我们提出了一个空间整流器，以估计倾斜图像的表面正态。倾斜的图像特别令人感兴趣，因为更多的视觉数据是由任意定向的传感器（例如车身/机器人安装的摄像机）捕获的。现有方法在预测表面正常的方面表现出有限的性能，因为它们是使用重力对准图像进行训练的。我们的两个主要假设是：（1）视觉场景布局指示重力方向； （2）并非所有表面都由训练数据的结构化分布而被学习的估计量平均表示，因此，每个倾斜图像的转换都比其他图像更响应于学习的估计器。我们设计了一个空间整流器，该空间整流器被学会地将倾斜图像的表面正态分布转换为与重力一致的训练数据分布相匹配的校准图像。与空间整流器一起，我们提出了一种新型的截断角损失，在较小的角度误差和与异常值的稳健性下提供了更强的梯度。最终的估计器的表现优于最先进的方法，其中包括数据增强基线，不仅在扫描仪和NYUV2上，而且在一个名为TILT-RGBD的新数据集上，其中包括相当多的滚动和音高摄像头运动。
In this paper, we present a spatial rectifier to estimate surface normals of tilted images. Tilted images are of particular interest as more visual data are captured by arbitrarily oriented sensors such as body-/robot-mounted cameras. Existing approaches exhibit bounded performance on predicting surface normals because they were trained using gravity-aligned images. Our two main hypotheses are: (1) visual scene layout is indicative of the gravity direction; and (2) not all surfaces are equally represented by a learned estimator due to the structured distribution of the training data, thus, there exists a transformation for each tilted image that is more responsive to the learned estimator than others. We design a spatial rectifier that is learned to transform the surface normal distribution of a tilted image to the rectified one that matches the gravity-aligned training data distribution. Along with the spatial rectifier, we propose a novel truncated angular loss that offers a stronger gradient at smaller angular errors and robustness to outliers. The resulting estimator outperforms the state-of-the-art methods including data augmentation baselines not only on ScanNet and NYUv2 but also on a new dataset called Tilt-RGBD that includes considerable roll and pitch camera motion.
