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A Fusion Network for Semantic Segmentation Using RGB-D Data

机译：使用RGB-D数据进行语义分割的融合网络

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Semantic scene parsing is considerable in many intelligent field, including perceptual robotics. For the past few years, pixel-wise prediction tasks like semantic segmentation with RGB images has been extensively studied and has reached very remarkable parsing levels, thanks to convolutional neural networks (CNNs) and large scene datasets. With the development of stereo cameras and RGBD sensors, it is expected that additional depth information will help improving accuracy. In this paper, we propose a semantic segmentation framework incorporating RGB and complementary depth information. Motivated by the success of fully convolutional networks (FCN) in semantic segmentation field, we design a fully convolutional networks consists of two branches which extract features from both RGB and depth data simultaneously and fuse them as the network goes deeper. Instead of aggregating multiple model, our goal is to utilize RGB data and depth data more effectively in a single model. We evaluate our approach on the NYU-Depth V2 dataset, which consists of 1449 cluttered indoor scenes, and achieve competitive results with the state-of-the-art methods.
机译：语义场景解析在许多智能领域都非常重要，包括感知机器人。在过去的几年中，由于卷积神经网络（CNN）和大型场景数据集，对RGB图像进行语义分割等像素级预测任务已经得到了广泛研究，并且达到了非常出色的解析水平。随着立体相机和RGBD传感器的发展，预计更多的深度信息将有助于提高准确性。在本文中，我们提出了一种结合RGB和互补深度信息的语义分割框架。受全卷积网络（FCN）在语义分割领域的成功推动，我们设计了一个由两个分支组成的全卷积网络，该两个分支同时从RGB和深度数据中提取特征，并随着网络的深入而融合。我们的目标不是汇总多个模型，而是在单个模型中更有效地利用RGB数据和深度数据。我们在NYU-Depth V2数据集上评估了我们的方法，该数据集由1449个混乱的室内场景组成，并通过最先进的方法获得了竞争性结果。

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