Edge-Aware Convolution for RGB-D Image Segmentation

Rongsen Chen, Fang-Lue Zhang, Taehyun Rhee

Abstract: Convolutional Neural Networks using RGB-D images as input have shown superior performance in recent research in the field of semantic segmentation. In RGB-D data, the depth channel encodes information from the 3D spatial domain, which has an inherent difference with the color channels. It thus needs to be treated in a special way, rather than just processed as another channel of the input signal. Under this purpose, we propose a simple but not trivial edge-aware convolutional kernel to utilize the geometric information contained in the depth channel to extract feature maps in a more effective manner. The edge-aware convolutional kernel is built upon regular convolutional kernel, thus, it can be used to restructure existing CNN models to achieve stable and effective feature extraction for RGB-D data. We compare our result with a previous method that is closely related to our to show our method can provide more effective and stable feature extraction.