Mobile devices usually mount a depth sensor to resolve ill-posed problems, like salient object detection on cluttered background. The main barrier of exploring RGBD data is to handle the information from two different modalities. To cope with this problem, in this paper, we propose a boundary-aware cross-modal fusion network for RGBD salient object detection. In particular, to enhance the fusion of color and depth features, we present a cross-modal feature sampling module to balance the contribution of the RGB and depth features based on the statistics of their channel values. In addition, in our multi-scale dense fusion network architecture, we not only incorporate edge-sensitive losses to preserve the boundary of the detected salient region, but also refine its structure by merging the estimated saliency maps of different scales. We accomplish the multi-scale saliency map merging using two alternative methods which produce refined saliency maps via per-pixel weighted combination and an encoder-decoder network. Extensive experimental evaluations demonstrate that our proposed framework can achieve the state-of-the-art performance on several public RGBD-based datasets.

中文翻译：

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具有跨模态特征采样的边界感知 RGBD 显着目标检测


移动设备通常安装深度传感器来解决不适定问题，例如杂乱背景上的显着物体检测。探索 RGBD 数据的主要障碍是处理来自两种不同模式的信息。为了解决这个问题，在本文中，我们提出了一种用于 RGBD 显着目标检测的边界感知跨模态融合网络。特别是，为了增强颜色和深度特征的融合，我们提出了一种跨模态特征采样模块，以根据通道值的统计来平衡 RGB 和深度特征的贡献。此外，在我们的多尺度密集融合网络架构中，我们不仅结合了边缘敏感损失来保留检测到的显着区域的边界，而且还通过合并不同尺度的估计显着图来细化其结构。我们使用两种替代方法完成多尺度显着性图合并，这两种方法通过每像素加权组合和编码器-解码器网络生成精炼的显着性图。广泛的实验评估表明，我们提出的框架可以在几个基于 RGBD 的公共数据集上实现最先进的性能。