论文标题
RGB-D摄像机的深度适应CNN
Depth-Adapted CNN for RGB-D cameras
论文作者
论文摘要
传统的2D卷积神经网络（CNN）通过应用线性过滤器从输入图像提取特征。这些过滤器通过加权固定邻域上的光度信息来计算空间连贯性，而无需考虑几何信息。我们通过使用RGB-D摄像机提供的深度信息来解决改善经典RGB CNN方法的问题。最先进的方法将深度用作附加通道或图像（HHA），或通过2D CNN到3D CNN。本文提出了一种新颖而通用的程序，以阐明CNN体系结构中的光度和几何信息。深度数据表示为2D偏移，以适应空间采样位置。提出的新模型是围绕相机坐标系的X和Y轴的缩放和旋转的不变性。此外，当深度数据恒定时，我们的模型等同于常规CNN。基准的实验验证了我们模型的有效性。
Conventional 2D Convolutional Neural Networks (CNN) extract features from an input image by applying linear filters. These filters compute the spatial coherence by weighting the photometric information on a fixed neighborhood without taking into account the geometric information. We tackle the problem of improving the classical RGB CNN methods by using the depth information provided by the RGB-D cameras. State-of-the-art approaches use depth as an additional channel or image (HHA) or pass from 2D CNN to 3D CNN. This paper proposes a novel and generic procedure to articulate both photometric and geometric information in CNN architecture. The depth data is represented as a 2D offset to adapt spatial sampling locations. The new model presented is invariant to scale and rotation around the X and the Y axis of the camera coordinate system. Moreover, when depth data is constant, our model is equivalent to a regular CNN. Experiments of benchmarks validate the effectiveness of our model.
