ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation

TL;DR

This paper introduces ShapeConv, a shape-aware convolutional layer that decomposes depth features into shape and base components, enhancing indoor RGB-D semantic segmentation accuracy without increasing inference complexity.

Contribution

The paper proposes a novel ShapeConv layer that explicitly models shape and base components of depth features, improving segmentation performance and seamlessly integrating into existing CNNs.

Findings

Significant improvement on NYU-Dv2, SUN RGB-D, and SID benchmarks.

Enhanced segmentation accuracy without additional inference cost.

Model-agnostic approach compatible with various CNN architectures.

Abstract

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.