RGB-D semantic segmentation has attracted increasing attention over the past few years. Existing methods mostly employ homogeneous convolution operators to consume the RGB and depth features, ignoring their intrinsic differences. In fact, the RGB values capture the photometric appearance properties in the projected image space, while the depth feature encodes both the shape of a local geometry as well as the base (whereabout) of it in a larger context. Compared with the base, the shape probably is more inherent and has a stronger connection to the semantics, and thus is more critical for segmentation accuracy. Inspired by this observation, we introduce a Shape-aware Convolutional layer (ShapeConv) for processing the depth feature, where the depth feature is firstly decomposed into a shape-component and a base-component, next two learnable weights are introduced to cooperate with them independently, and finally a convolution is applied on the re-weighted combination of these two components. ShapeConv is model-agnostic and can be easily integrated into most CNNs to replace vanilla convolutional layers for semantic segmentation. Extensive experiments on three challenging indoor RGB-D semantic segmentation benchmarks, i.e., NYU-Dv2(-13,-40), SUN RGB-D, and SID, demonstrate the effectiveness of our ShapeConv when employing it over five popular architectures. Moreover, the performance of CNNs with ShapeConv is boosted without introducing any computation and memory increase in the inference phase. The reason is that the learnt weights for balancing the importance between the shape and base components in ShapeConv become constants in the inference phase, and thus can be fused into the following convolution, resulting in a network that is identical to one with vanilla convolutional layers.
翻译:过去几年来, RGB- D 语义断裂已引起越来越多的关注 。 现有方法大多使用同质变动操作器来消耗 RGB 和深度特性, 忽略了它们的内在差异 。 事实上, RGB 值捕捉了预测图像空间的光度外观属性, 而深度特性则将本地几何形状和基底( 位置) 编码成一个更大的背景。 与基底相比, 形状可能更具有内在性, 与语义联系更紧密, 因而对分解准确性来说更为关键 。 受此观察的启发, 我们引入了一个 Shape-aware 变异层, 我们引入了 Shabe 变异性 和 变异性 。 变异 变异 变异 变变 变 和 变异性 变异性变异, 变异性变异性变异性变异性变异性变异性 。 变异性变异性变异性变异性 变异性变异性变异性变变异性变异性变异性变异性变变异性, 变异性变异性变异性变异性变异性变性变性变异性变异性变异性变性变性变变变变变变变变变变变变变变变变变变变变变变变变性变变变变变变变变变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变变性,, 。 变性变性变异性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变变变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变性变变性变