English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

RGB-D semantic segmentation has attracted increasing attention over the pastfew years. Existing methods mostly employ homogeneous convolution operators toconsume the RGB and depth features, ignoring their intrinsic differences. Infact, the RGB values capture the photometric appearance properties in theprojected image space, while the depth feature encodes both the shape of alocal 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 strongerconnection to the semantics, and thus is more critical for segmentationaccuracy. Inspired by this observation, we introduce a Shape-awareConvolutional layer (ShapeConv) for processing the depth feature, where thedepth feature is firstly decomposed into a shape-component and abase-component, next two learnable weights are introduced to cooperate withthem independently, and finally a convolution is applied on the re-weightedcombination of these two components. ShapeConv is model-agnostic and can beeasily integrated into most CNNs to replace vanilla convolutional layers forsemantic segmentation. Extensive experiments on three challenging indoor RGB-Dsemantic segmentation benchmarks, i.e., NYU-Dv2(-13,-40), SUN RGB-D, and SID,demonstrate the effectiveness of our ShapeConv when employing it over fivepopular architectures. Moreover, the performance of CNNs with ShapeConv isboosted without introducing any computation and memory increase in theinference phase. The reason is that the learnt weights for balancing theimportance between the shape and base components in ShapeConv become constantsin the inference phase, and thus can be fused into the following convolution,resulting in a network that is identical to one with vanilla convolutionallayers.

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